all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-26T02:43:08Z | Official PyTorch implementation for the ICML 2023 paper "Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships". | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 3 | 645,599,366 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | ymguo21/FedIIR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2023-10-31T09:47:22Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 14 | [] | 2026-05-19T06:35:31Z | https://github.com/ymguo21/FedIIR | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-26T05:42:30Z | The official implementation for paper: Improving Knowledge Distillation via Regularizing Feature Norm and Direction | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 4 | 645,642,327 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | WangYZ1608/Knowledge-Distillation-via-ND | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-08-03T02:38:39Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 25 | [
"computer-vision",
"imagenet",
"knowledge-distillation",
"pytorch"
] | 2026-09-16T06:35:10Z | https://github.com/WangYZ1608/Knowledge-Distillation-via-ND | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-26T12:26:42Z | [NeurIPS 2023] Official PyTorch implementation for the paper "CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 19 | 645,783,018 | null | Python | null | [
"diffusion"
] | yujiwen/CRoSS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2024-06-12T02:56:27Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 156 | [] | 2026-08-20T21:14:58Z | https://github.com/yujiwen/CRoSS | null |
[
"generative-modeling",
"machine-learning",
"reliable-ml",
"robust-ml",
"trustworthy-ml"
] | [
"diffusion",
"out-of-distribution-detection",
"uncertainty-estimation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"probable.evaluation-ood-readme",
"trust.out-of-distribution-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-26T19:30:34Z | The official code release for Unsupervised Out-of-distribution Detection with Diffusion Inpainting (ICML 2023) | [
"generative-modeling",
"machine-learning",
"reliable-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 645,928,987 | https://arxiv.org/abs/2302.10326 | Python | Apache-2.0 | [
"diffusion",
"out-of-distribution-detection",
"uncertainty-estimation"
] | zhenzhel/lift_map_detect | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-11T16:48:52Z | [] | 2023-08-16T20:17:20Z | [
"probable.evaluation-ood-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 29 | [] | 2026-08-01T15:07:05Z | https://github.com/zhenzhel/lift_map_detect | null |
[
"causal-ml",
"reinforcement-learning"
] | [
"causal-discovery",
"reinforcement-learning",
"structure-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.causal-discovery"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-28T14:09:33Z | code for our paper "STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction" | [
"causal-ml",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 646,472,586 | null | Jupyter Notebook | null | [
"causal-discovery",
"reinforcement-learning",
"structure-learning"
] | paras2612/STREAMS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:32:04Z | [] | 2023-06-01T03:44:22Z | [
"trust.causal-discovery"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2024-11-05T14:45:48Z | https://github.com/paras2612/STREAMS | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-28T18:14:28Z | Official implementation for the paper "Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering" | [
"deep-learning",
"graph-learning"
] | [
"neural-network",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 2 | 646,541,070 | null | Java | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | Morteza-24/Monolith-to-Microservice | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-06-13T00:35:30Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-06-13T00:35:34Z | https://github.com/Morteza-24/Monolith-to-Microservice | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-29T03:20:03Z | The official implementation for paper: Improving Knowledge Distillation via Regularizing Feature Norm and Direction | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 7 | 646,654,358 | null | null | MIT | [
"distillation",
"knowledge-distillation"
] | lliai/Knowledge-Distillation-via-ND | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-05-26T06:32:31Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-05-29T03:20:03Z | https://github.com/lliai/Knowledge-Distillation-via-ND | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-29T06:57:18Z | Official code for ICML'23 paper [Model-based Offline Reinforcement Learning with Count-based Conservatism] | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 1 | 646,709,916 | null | Python | MIT | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | oh-lab/Count-MORL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-05-29T06:59:02Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2025-11-28T08:14:34Z | https://github.com/oh-lab/Count-MORL | null |
[
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-29T17:25:33Z | Official Code for ICML 2023 Paper: On the Generalization of Multi-modal Contrastive Learning | [
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 2 | 646,936,769 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | PKU-ML/CLIP-Help-SimCLR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2023-11-15T12:00:50Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 26 | [] | 2025-08-04T06:13:01Z | https://github.com/PKU-ML/CLIP-Help-SimCLR | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-30T07:47:07Z | Official implementation of ICML 2023 paper "FeDXL: Provable Federated Learning for Deep X-Risk Optimization". | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 647,166,399 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | Optimization-AI/ICML2023_FeDXL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2023-05-30T08:00:35Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2023-07-10T11:54:20Z | https://github.com/Optimization-AI/ICML2023_FeDXL | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"interpretability-and-safety"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-30T14:51:02Z | Official implementation of the paper The Hidden Language of Diffusion Models | [
"generative-ai",
"generative-modeling",
"interpretability-and-safety"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 647,332,731 | https://hila-chefer.github.io/Conceptor/ | null | null | [
"diffusion"
] | hila-chefer/Conceptor | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-05T20:10:04Z | [
"2306.00966"
] | 2024-01-24T01:14:09Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 79 | [
"explainability",
"explainable-ai",
"generative-model",
"stable-diffusion"
] | 2026-09-30T20:31:17Z | https://github.com/hila-chefer/Conceptor | null |
[
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"paper-reference",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-30T22:26:24Z | [NeurIPS 2023] Official Implementation: "Ambient Diffusion: Learning Clean Distributions from Corrupted Data" | [
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 17 | 647,487,828 | https://arxiv.org/abs/2305.19256 | Python | null | [
"diffusion"
] | giannisdaras/ambient-diffusion | [
"description",
"github-topics",
"paper-reference",
"repository-metadata"
] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2023-10-23T04:49:35Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 92 | [
"diffusion-models"
] | 2026-10-01T15:56:55Z | https://github.com/giannisdaras/ambient-diffusion | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-31T03:20:38Z | The official implementation of DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design (ICML 2023) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 10 | 647,557,409 | null | Python | NOASSERTION | [
"diffusion"
] | bytedance/DecompDiff | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2023-07-27T23:24:40Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 72 | [
"research"
] | 2026-08-29T22:23:43Z | https://github.com/bytedance/DecompDiff | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-31T08:19:06Z | The official source code for Similarity Preserving Adversarial Graph Contrastive Learning (SP-AGCL) at KDD 2023. | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 647,646,210 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | yeonjun-in/torch-SP-AGCL | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-21T15:48:06Z | [] | 2024-01-18T01:56:51Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [] | 2025-01-21T10:54:56Z | https://github.com/yeonjun-in/torch-SP-AGCL | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
"meta-learning",
"parameter-efficient-fine-tuning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-31T12:27:48Z | Official implementation of 'Adapting to Inter-Subject Variability in EEG-Based Motor Imagery Classification with Meta-Learning' (ICLR 2023). Our meta-learning approach is compared to transfer learning in terms of accuracy after few-shot fine-tuning on new subjects for EEG-based motor imagery classification. | [
"generative-ai",
"language"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 647,741,213 | null | Jupyter Notebook | MIT | [
"fine-tuning",
"meta-learning",
"parameter-efficient-fine-tuning"
] | arnav-pati/iclr2023-meta-learning-eeg-mi-classification | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2023-06-11T15:21:59Z | [
"llm.finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 17 | [] | 2026-07-23T02:56:39Z | https://github.com/arnav-pati/iclr2023-meta-learning-eeg-mi-classification | null |
[
"computer-vision",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning",
"robot-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-05-31T17:13:43Z | Code for NeurIPS 2023 paper "Active Vision Reinforcement Learning with Limited Visual Observability" | [
"computer-vision",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 2 | 647,859,297 | https://elicassion.github.io/sugarl/sugarl.html | Python | null | [
"reinforcement-learning",
"robot-learning"
] | elicassion/sugarl | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2024-10-10T02:08:14Z | [
"robotics.robotics-topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 56 | [
"active-reinforcement-learning",
"active-vision",
"gym",
"reinforcement-learning",
"robotics"
] | 2026-05-27T07:50:32Z | https://github.com/elicassion/sugarl | null |
[
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"machine-translation",
"neural-machine-translation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling",
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-01T02:11:16Z | Official code for IWSLT 2023 paper Language Model Based Target Token Importance Rescaling for Simultaneous Neural Machine Translation | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 648,004,812 | null | null | null | [
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] | sfu-natlang/target_rescale_siMT | [
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] | 3 | 2026-10-31T16:49:26Z | [] | 2023-06-01T02:11:16Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-06-01T02:11:16Z | https://github.com/sfu-natlang/target_rescale_siMT | null |
[
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] | [
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] | [
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"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-01T12:39:18Z | An official pytorch implementation of AAAI 2024 paper "Latent Space Editing in Transformer-based Flow Matching" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 1 | 648,202,646 | https://taohu.me/lfm/ | Python | MIT | [
"diffusion",
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] | dongzhuoyao/uspace | [
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"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2024-04-10T21:09:32Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 52 | [
"diffusion-models",
"flow-matching",
"image-editing",
"stochastic-interpolant"
] | 2026-07-07T13:56:06Z | https://github.com/dongzhuoyao/uspace | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["flow-matching"]} |
[
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] | [
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] | [
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] | [
"llm.diffusion-phrase",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-02T01:34:09Z | code for paper "Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 7 | 648,440,836 | null | Python | MIT | [
"diffusion"
] | OPPO-Mente-Lab/attention-mask-control | [
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"repository-metadata"
] | 3 | 2026-10-04T15:58:58Z | [] | 2023-09-21T11:52:14Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 46 | [] | 2026-01-18T08:41:27Z | https://github.com/OPPO-Mente-Lab/attention-mask-control | null |
[
"deep-learning",
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] | [
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] | [
"description",
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] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-03T05:45:08Z | Official Implementation of our KDD 2023 Paper: PERT-GNN: Latency Prediction for Microservice-based Cloud-Native Applications via Graph Neural Networks. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 1 | 648,892,903 | null | Python | null | [
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] | handasontam/PERT-GNN-KDD23 | [
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] | 1 | 2026-10-10T16:53:26Z | [] | 2023-11-27T07:28:57Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 16 | [] | 2026-09-21T04:32:32Z | https://github.com/handasontam/PERT-GNN-KDD23 | null |
[
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] | [
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-03T16:01:15Z | The official implementation of the paper "Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 649,049,488 | null | Python | null | [
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] | ygjin11/task-hypernet | [
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] | 1 | 2026-10-23T15:59:01Z | [] | 2024-02-27T09:49:40Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 12 | [] | 2025-07-23T08:30:46Z | https://github.com/ygjin11/task-hypernet | null |
[
"efficient-ml",
"generative-ai",
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] | [
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] | [
"efficiency.pruning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-04T16:33:21Z | Official implementation of INDIN 2023 paper "Network Pruning and Fine-tuning for Few-shot Industrial Image Anomaly Detection" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 649,371,729 | null | null | null | [
"neural-network-pruning",
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] | zhangjieTHU/Network-Pruning-and-Fine-tuning-for-Few-shot-Industrial-Image-Anomaly-Detection | [
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] | 2 | 2026-10-19T17:46:36Z | [] | 2023-11-16T12:50:11Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-06-04T16:41:20Z | https://github.com/zhangjieTHU/Network-Pruning-and-Fine-tuning-for-Few-shot-Industrial-Image-Anomaly-Detection | null |
[
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] | [
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] | [
"general.novel-method",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-05T06:39:14Z | In this study, we propose a novel method that Combination Graph Neural Network (CGNN) and Bidirectional Long Short-Term Memory (BiLSTM) to predict drug and target affinity (DTA). | [
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"graph-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 649,562,043 | null | Python | null | [
"graph-neural-network",
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"message-passing",
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] | fyfly1114/CGBiLSTM | [
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"repository-metadata"
] | 3 | 2026-10-11T16:48:52Z | [] | 2023-06-05T06:53:30Z | [
"graph.gnn-description"
] | 2f00d531598e43797f001db9af1b8386934c9f27 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "2f00d531598e43797f001db9af1b8386934c9f27" | [
"installation",
"method",
"other"
] | [
"method-contribution",
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] | ok | 2026-09-28T20:05:39Z | fyfly1114/CGBiLSTM | specific-method-with-novelty-claim | [
"method-contribution",
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] | include | ml-contribution-v5 | 0 | [] | 2023-06-05T06:40:26Z | https://github.com/fyfly1114/CGBiLSTM | null |
[
"distributed-ml",
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] | [
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] | [
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"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-06T17:29:39Z | Code for our paper "Guiding The Last Layer in Federated Learning with Pre-Trained Models" | [
"distributed-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 650,260,323 | null | Python | MIT | [
"collaborative-learning",
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] | GwenLegate/GuidingLastLayerFLPretrain | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2024-02-25T02:23:05Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 9 | [] | 2025-10-12T00:23:30Z | https://github.com/GwenLegate/GuidingLastLayerFLPretrain | null |
[
"deep-learning",
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] | [
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] | [
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"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-07T08:32:48Z | [Knowledge-Based Systems 2023] The official code of paper, "Causal Diffused Graph-Transformer Network with Stacked Early Classification Loss for Efficient Stream Classification of Rumours". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 650,517,208 | https://www.sciencedirect.com/science/article/pii/S0950705123005579 | Python | null | [
"graph-transformer",
"message-passing",
"transformer"
] | thcheung/CDGTN | [
"description",
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"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-07-18T10:01:38Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 6 | [] | 2026-08-11T16:10:43Z | https://github.com/thcheung/CDGTN | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal",
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] | [
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] | [
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"repository-metadata"
] | [
"llm.preference-optimization",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-07T08:44:09Z | [NeurIPS 2023] Official Code for "SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation" | [
"computer-vision",
"generative-ai",
"language",
"multimodal",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 83 | 650,521,566 | https://caizhongang.github.io/projects/SMPLer-X/ | Python | NOASSERTION | [
"alignment",
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] | MotrixLab/SMPLer-X | [
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"query-match",
"repository-metadata"
] | 5 | 2026-10-04T15:44:14Z | [] | 2026-02-12T08:55:28Z | [
"llm.preference-optimization",
"multimodal.motion-generation"
] | eefa5ae6f35590a859957fdb10b4852e08cc10ac | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "eefa5ae6f35590a859957fdb10b4852e08cc10ac" | [
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] | [
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] | ok | 2026-09-27T17:16:20Z | MotrixLab/SMPLer-X | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
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] | include | ml-contribution-v5 | 1,228 | [] | 2026-09-30T01:13:05Z | https://github.com/MotrixLab/SMPLer-X | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-07T09:54:12Z | This repository is the official implementation of our CAMB (Contrastive Learning-Enhanced Label Correlation Learning for Multi-Label Text Classification) paper. | [
"natural-language-processing"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 650,548,306 | null | null | null | [
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] | Qianhu-233/CAMB | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2023-06-18T14:25:41Z | [
"nlp.text-classification"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-06-07T09:54:12Z | https://github.com/Qianhu-233/CAMB | null |
[
"multimodal",
"reinforcement-learning",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-07T13:06:01Z | Official code for "Reward-Free Curricula for Training Robust World Models", ICLR 2024. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 2 | 650,622,747 | null | Python | MIT | [
"environment-modeling",
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] | marc-rigter/waker | [
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"repository-metadata"
] | 2 | 2026-10-05T19:54:32Z | [] | 2024-01-24T12:23:52Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 29 | [] | 2026-05-12T08:09:08Z | https://github.com/marc-rigter/waker | null |
[
"multi-agent-systems",
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-11T07:26:40Z | Official implementation for ICML 2024 paper "HGAP: Boosting Permutation Invariant and Permutation Equivariant in Multi-Agent Reinforcement Learning via Graph Attention Network". | [
"multi-agent-systems",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 652,130,999 | https://proceedings.mlr.press/v235/lin24m.html | Python | MIT | [
"centralized-training",
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] | ChristianLin0420/HGAP | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2024-02-04T13:13:30Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 10 | [] | 2026-07-24T02:59:37Z | https://github.com/ChristianLin0420/HGAP | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-11T09:53:05Z | This is official implementation of the paper "Semi-supervised Reference based sketch extraction using a contrastive learning framework" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 3 | 652,166,336 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | Chanuku/Semi_ref2sketch_code | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2023-09-10T15:48:44Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 37 | [] | 2025-11-06T04:56:46Z | https://github.com/Chanuku/Semi_ref2sketch_code | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-12T08:33:55Z | The is the official implementation of ICML 2023 paper "Revisiting Weighted Aggregation in Federated Learning with Neural Networks". | [
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] | [
"deep-learning",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:44:14Z | false | 9 | 652,519,410 | null | Python | MIT | [
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"neural-network"
] | ZexiLee/ICML-2023-FedLAW | [
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] | 3 | 2026-10-08T18:15:14Z | [] | 2023-08-28T09:10:21Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 67 | [
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"federated-learning",
"model-fusion",
"weight-decay"
] | 2026-07-03T15:36:47Z | https://github.com/ZexiLee/ICML-2023-FedLAW | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-12T08:56:42Z | [NeurIPS 2023] Official implementation of SyncDiffusion | [
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"generative-ai",
"generative-modeling"
] | [
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"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 11 | 652,528,284 | https://syncdiffusion.github.io/ | Jupyter Notebook | MIT | [
"diffusion",
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] | KAIST-Visual-AI-Group/SyncDiffusion | [
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"github-topics",
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"repository-metadata"
] | 3 | 2026-10-03T15:32:04Z | [] | 2024-04-20T15:25:25Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 170 | [
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"stable-diffusion",
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] | 2026-09-14T12:10:56Z | https://github.com/KAIST-Visual-AI-Group/SyncDiffusion | null |
[
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] | [
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] | [
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-12T10:50:31Z | Matlab code performing the finite element simulation of an electrolyte contained within a phase-field representation of cracks. A novel diffusion model is used to represent the electrolyte within phase-field fractures, allowing for accurate predictions of the hydrogen uptake while also simulating propagating cracks. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 4 | 652,572,643 | https://doi.org/10.1016/j.cma.2023.116235 | MATLAB | null | [
"diffusion"
] | T-Hageman/PhysBased_Diffusion_Embrittlement | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2023-07-21T09:05:31Z | [
"llm.diffusion-phrase"
] | d8e6cd4d282c5c7008e7820769f05b04791d4789 | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "d8e6cd4d282c5c7008e7820769f05b04791d4789" | [
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-10-06T18:32:27Z | T-Hageman/PhysBased_Diffusion_Embrittlement | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 17 | [] | 2026-02-27T09:05:51Z | https://github.com/T-Hageman/PhysBased_Diffusion_Embrittlement | null |
[
"chemistry",
"deep-learning",
"graph-learning",
"materials-science"
] | [
"graph-neural-network",
"materials-discovery",
"message-passing",
"neural-network",
"property-prediction"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"materials-crystal-graph"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-12T19:41:33Z | Code for paper: Substitutional Alloying Using Crystal Graph Neural Networks | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 652,783,617 | https://arxiv.org/abs/2306.10766 | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | danielcieslinski/suballoy | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:48:52Z | [] | 2023-08-07T22:46:14Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-09-01T08:31:49Z | https://github.com/danielcieslinski/suballoy | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting",
"transportation"
] | [
"spatio-temporal-forecasting",
"traffic-forecasting",
"transformer"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.traffic-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-14T09:03:59Z | [CIKM'23] Official code for our paper "Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting". | [
"forecasting",
"time-series",
"time-series-and-forecasting",
"transportation"
] | [
"embedding",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 33 | 653,547,121 | https://arxiv.org/abs/2308.10425 | Python | null | [
"spatio-temporal-forecasting",
"traffic-forecasting",
"transformer"
] | XDZhelheim/STAEformer | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-05T19:54:32Z | [] | 2025-11-15T07:30:54Z | [
"timeseries.traffic-forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 245 | [] | 2026-09-26T07:17:54Z | https://github.com/XDZhelheim/STAEformer | null |
[
"3d-vision",
"computer-vision",
"general-ml",
"generative-ai",
"geometric-learning",
"language"
] | [
"deep-learning",
"language-model",
"point-cloud-learning",
"point-cloud-segmentation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"graph.point-clouds",
"llm.transformers",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-15T16:02:44Z | Official PyTorch implementation of Superpoint Transformer [ICCV'23], SuperCluster [3DV'24 Oral], and EZ-SP [ICRA'26] | [
"computer-vision",
"general-ml"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 138 | 654,209,013 | null | Python | MIT | [
"deep-learning",
"transformer"
] | drprojects/superpoint_transformer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-22T14:58:59Z | [] | 2026-10-21T13:16:53Z | [
"general.topic-deep-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1,068 | [
"3d",
"3dv2024",
"deep-learning",
"efficient",
"fast",
"graph-clustering",
"hierarchical",
"iccv2023",
"lightweight",
"panoptic-segmentation",
"partition",
"partitioning",
"point-cloud",
"pytorch",
"semantic-segmentation",
"superpoint",
"transformer"
] | 2026-10-21T08:04:21Z | https://github.com/drprojects/superpoint_transformer | null |
[
"computer-vision"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.self-supervised-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-16T13:35:25Z | [CVPR'23] Official PyTorch implementation of Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation | [
"computer-vision"
] | [
"self-supervised-learning",
"classifier",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 654,598,244 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | dahyun-kang/cst | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-05T19:54:32Z | [] | 2023-10-22T08:32:20Z | [
"vision.self-supervised-vision"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 46 | [] | 2025-11-01T10:48:58Z | https://github.com/dahyun-kang/cst | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-16T16:01:07Z | Code for paper "Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach". | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 654,654,767 | https://doi.org/10.1080/15472450.2024.2324385 | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | ytj254/TSP-DQN | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-09-22T15:09:23Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-02-19T23:38:47Z | https://github.com/ytj254/TSP-DQN | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-16T19:10:10Z | official code for CVPR 2023 paper 'Change-Aware Sampling and Contrastive Learning for Satellite Images' | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 7 | 654,715,157 | null | Jupyter Notebook | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | utkarshmall13/CACo | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-21T15:48:06Z | [] | 2024-04-04T01:35:55Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 59 | [] | 2026-05-08T07:27:10Z | https://github.com/utkarshmall13/CACo | null |
[
"computer-vision",
"generative-ai"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-17T11:14:28Z | Official implementation of AnimateDiff. | [
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1,104 | 654,930,782 | https://animatediff.github.io | Python | Apache-2.0 | [
"diffusion"
] | guoyww/AnimateDiff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 12 | 2026-10-23T15:59:01Z | [] | 2024-07-31T01:14:15Z | [
"recall.name.diffusion-model"
] | f8fdf3b1667ccd8e055f80826d5528ce5d3703f1 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "f8fdf3b1667ccd8e055f80826d5528ce5d3703f1" | [
"citation",
"installation",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | guoyww/AnimateDiff | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 12,272 | [] | 2026-10-23T06:07:43Z | https://github.com/guoyww/AnimateDiff | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-17T13:18:49Z | Code for paper "Enhancing Robustness of Deep Reinforcement Learning Based Adaptive Traffic Signal Controllers in Mixed Traffic Environments Through Data Fusion and Multi-Discrete Actions". | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 654,963,578 | https://doi.org/10.1109/TITS.2024.3399066 | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | ytj254/TSC_PPO_Multidiscrete-action | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-09-22T14:57:03Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-02-19T23:37:48Z | https://github.com/ytj254/TSC_PPO_Multidiscrete-action | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-19T01:08:04Z | Code repo for our paper entitled "Uncertainty and Shape-Aware Continual Test-Time Adaptation for Cross-Domain Segmentation of Medical Images" | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 655,468,955 | null | null | null | [
"adaptation",
"test-time-adaptation"
] | ThisGame42/CTTA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2024-01-10T04:05:47Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2025-10-19T07:18:37Z | https://github.com/ThisGame42/CTTA | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning",
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-19T08:41:18Z | The official PyTorch implementation of the paper "MotionGPT: Finetuned LLMs are General-Purpose Motion Generators" | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 15 | 655,609,085 | https://qiqiapink.github.io/MotionGPT/ | Python | null | [
"fine-tuning",
"instruction-tuning",
"language-model",
"pretraining"
] | qiqiApink/MotionGPT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2023-12-28T06:32:18Z | [
"llm.instruction-tuning",
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 237 | [
"3d-human",
"gpt",
"large-language-models",
"motion",
"motion-generation",
"motiongpt",
"text-to-motion"
] | 2026-07-23T07:22:25Z | https://github.com/qiqiApink/MotionGPT | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-21T17:08:22Z | DDRM-PR paper, official implementation. Phase retrieval with pretrained diffusion models | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 656,802,516 | null | Python | BSD-3-Clause | [
"diffusion"
] | METU-SPACE-Lab/DDRM-PR-for-Fourier-Phase-Retrieval | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-07-09T12:14:56Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [
"computational-imaging",
"diffusion",
"holography",
"inverse-problems",
"phase-recovery",
"phase-retrieval",
"ptychography"
] | 2026-09-24T07:48:30Z | https://github.com/METU-SPACE-Lab/DDRM-PR-for-Fourier-Phase-Retrieval | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-21T21:00:55Z | Official implementation of the paper "TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 656,883,015 | null | null | MIT | [
"diffusion"
] | MIvanovska/TomatoDIFF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2023-10-30T10:07:16Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2025-11-19T11:35:53Z | https://github.com/MIvanovska/TomatoDIFF | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-23T14:17:03Z | [TMLR] Official PyTorch implementation of paper "Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 657,648,303 | null | Python | MIT | [
"quantization"
] | HuangOwen/Quantization-Variation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-09-27T23:58:17Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 49 | [] | 2026-08-20T21:15:19Z | https://github.com/HuangOwen/Quantization-Variation | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-24T12:32:24Z | [NIPS 2023] Official implementation for "DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models" https://arxiv.org/abs/2306.14685 | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 27 | 657,991,189 | https://ximinng.github.io/DiffSketcher-project/ | Python | MIT | [
"diffusion"
] | ximinng/DiffSketcher | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-02-24T08:02:33Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 310 | [
"sketch",
"sketch-generation",
"text-to-sketch",
"text-to-svg"
] | 2026-09-14T12:18:05Z | https://github.com/ximinng/DiffSketcher | null |
[
"generative-ai",
"generative-modeling",
"health-and-biomedicine"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-25T02:33:33Z | Official implementation of the paper "DiffSDS: A Language Diffusion Model for Protein Backbone Inpainting under Geometric Conditions and Constraints". | [
"generative-ai",
"generative-modeling",
"health-and-biomedicine"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 2 | 658,177,111 | null | Jupyter Notebook | null | [
"diffusion"
] | A4Bio/DiffSDS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2023-06-25T02:43:19Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 14 | [] | 2024-12-12T07:41:56Z | https://github.com/A4Bio/DiffSDS | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-25T03:56:39Z | The official implementation of the paper "Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models" (ICML 2023 Workshop on Challenges in Deployable Generative AI) | [
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"generative-modeling"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 658,192,747 | https://nannullna.github.io/safe-diffusion/ | Python | MIT | [
"diffusion",
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] | nannullna/safe-diffusion | [
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"query-match",
"repository-metadata"
] | 3 | 2026-10-06T17:10:23Z | [] | 2024-03-19T04:14:54Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 21 | [] | 2026-05-06T06:56:53Z | https://github.com/nannullna/safe-diffusion | null |
[
"generative-ai",
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"vision"
] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"llm.diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-25T10:51:23Z | An official pytorch implementation of EACL2024 short paper "Flow Matching for Conditional Text Generation in a Few Sampling Steps" | [
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"natural-language-processing",
"vision"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 3 | 658,298,255 | https://taohu.me/project_flowseq | Python | MIT | [
"diffusion",
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] | dongzhuoyao/flowseq | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-03T15:32:04Z | [] | 2025-07-17T07:46:23Z | [
"llm.diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 34 | [
"diffusion-model",
"flow",
"flow-matching"
] | 2026-07-07T13:55:48Z | https://github.com/dongzhuoyao/flowseq | null |
[
"distributed-ml",
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] | [
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] | [
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"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-26T08:03:58Z | The official Pytorch implementation of paper "FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation" accepted by MICCAI 2023 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 658,645,011 | null | Python | null | [
"collaborative-learning",
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] | ubc-tea/FedSoup | [
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"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2024-04-14T07:21:21Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 18 | [] | 2025-11-23T11:55:53Z | https://github.com/ubc-tea/FedSoup | null |
[
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"3d-vision",
"computer-vision",
"generative-ai",
"generative-modeling",
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] | [
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"diffusion",
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] | [
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] | [
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"multimodal.text-to-3d",
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-26T21:08:38Z | [ICLR'24] Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors | [
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"computer-vision",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 97 | 658,954,791 | https://guochengqian.github.io/project/magic123 | Jupyter Notebook | Apache-2.0 | [
"3d-perception",
"depth-estimation",
"diffusion"
] | guochengqian/Magic123 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 11 | 2026-10-17T13:17:44Z | [
"2306.17843"
] | 2026-07-06T02:45:31Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 1,623 | [
"3d-generation",
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"image-to-3d"
] | 2026-10-10T03:21:15Z | https://github.com/guochengqian/Magic123 | null |
[
"deep-learning",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-27T11:30:57Z | Official implementation of "Weakly-supervised positional contrastive learning: application to cirrhosis classification", MICCAI 2023 | [
"deep-learning",
"representation-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 2 | 659,222,415 | null | Python | NOASSERTION | [
"contrastive-learning",
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] | Guerbet-AI/wsp-contrastive | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-12-16T22:54:45Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 11 | [] | 2025-12-30T17:50:06Z | https://github.com/Guerbet-AI/wsp-contrastive | null |
[
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"generative-modeling",
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-27T19:54:18Z | Official implementation of “LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion”, MICCAI 2023 | [
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"generative-modeling",
"image-processing",
"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 5 | 659,421,285 | null | Python | MIT | [
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"low-light-enhancement"
] | longbai1006/LLCaps | [
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"repository-metadata"
] | 2 | 2026-10-03T15:32:04Z | [] | 2024-07-07T14:05:10Z | [
"recall26.low-light-enhancement"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 21 | [] | 2026-05-21T04:31:25Z | https://github.com/longbai1006/LLCaps | null |
[
"asset-allocation",
"chemistry",
"drug-discovery",
"finance",
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"portfolio-optimization",
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] | [
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"policy-learning",
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] | [
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] | [
"applied.portfolio-optimization",
"chem-drug-discovery",
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-28T15:26:59Z | Implementation of the model from "Faster sorting algorithms discovered using deep reinforcement learning" that discovered an all-new ultra fast sorting algorithm. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 1 | 659,799,277 | null | Python | MIT | [
"deep-reinforcement-learning",
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] | kyegomez/AlphaDev | [
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"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2023-08-29T00:52:37Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 11 | [] | 2025-12-22T16:00:41Z | https://github.com/kyegomez/AlphaDev | null |
[
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"machine-learning",
"machine-learning-systems",
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-06-29T04:09:15Z | Code for paper "Deep Reinforcement Learning based Multi-task Automated Channel Pruning for DNNs" | [
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] | [
"deep-learning",
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T19:54:32Z | false | 1 | 660,017,030 | https://fangvv.github.io/MTACP/ | Python | null | [
"fine-tuning",
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] | fangvv/MTACP | [
"description",
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] | 5 | 2026-10-31T15:48:22Z | [] | 2026-10-08T14:54:58Z | [
"probable.adaptation-finetuning-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [
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"neural-networks",
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] | 2026-10-08T14:55:08Z | https://github.com/fangvv/MTACP | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-01T15:35:54Z | Research project on natural language-based information retrieval using Sentence-BERT and a novel quadruplet loss to model partial semantic inclusion. | [
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"information-retrieval",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 661,036,852 | null | Jupyter Notebook | GPL-3.0 | [
"image-retrieval",
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] | lucastrefezza/quadruplet-sentence-transformer | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2026-04-07T17:54:26Z | [
"vision.image-retrieval"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
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] | include | ml-contribution-v5 | 1 | [] | 2026-04-07T17:59:54Z | https://github.com/lucastrefezza/quadruplet-sentence-transformer | null |
[
"computer-vision",
"deep-learning",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"vision.image-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-03T04:12:07Z | [ECAI 2023] Official PyTorch implementation of the paper "Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification" | [
"computer-vision",
"deep-learning",
"representation-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 4 | 661,521,104 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | yu-gi-oh-leilei/SADCL | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2023-11-12T14:58:51Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 23 | [] | 2026-03-06T08:42:53Z | https://github.com/yu-gi-oh-leilei/SADCL | null |
[
"reinforcement-learning"
] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-04T09:18:28Z | The python code for paper "Multi-objective Deep Reinforcement Learning for Mobile Edge Computing" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 16 | 662,061,793 | null | Python | MIT | [
"deep-reinforcement-learning",
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] | gracefulning/mec_morl_multipolicy | [
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"query-match",
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] | 3 | 2026-10-23T15:59:01Z | [] | 2023-07-04T09:32:05Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 126 | [] | 2026-10-15T14:32:03Z | https://github.com/gracefulning/mec_morl_multipolicy | null |
[
"deep-learning",
"graph-learning"
] | [
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"message-passing",
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] | [
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"license-metadata",
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] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-06T06:05:52Z | Building on my previous research and trying to create a A novel architecture combining Fourier analytic decomposition, graph-neural-network structural priors, and Transformer self-attention for state-of-the-art EEG emotion classification. | [
"deep-learning",
"graph-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 662,898,349 | https://ieeexplore.ieee.org/document/10627883 | Jupyter Notebook | BSD-3-Clause | [
"graph-neural-network",
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] | samvardhan03/EEG-project | [
"description",
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] | 1 | 2026-10-11T16:48:52Z | [] | 2026-02-16T23:16:11Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 4 | [
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"neural-network"
] | 2026-05-04T11:53:39Z | https://github.com/samvardhan03/EEG-project | null |
[
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"language",
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-07T15:44:18Z | [EMNLP 2024] Official Implementation of DisGeM: Distractor Generation for Multiple Choice Question with Span Masking | [
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] | [
"deep-learning",
"large-language-model",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 0 | 663,568,688 | null | Python | NOASSERTION | [
"natural-language-inference",
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] | obss/disgem | [
"description",
"github-topics",
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"repository-metadata"
] | 3 | 2026-10-08T18:15:14Z | [] | 2024-10-14T13:32:38Z | [
"nlp-natural-language-inference"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 6 | [
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"transformers"
] | 2025-11-06T10:40:29Z | https://github.com/obss/disgem | null |
[
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-artificial-intelligence"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-08T06:11:13Z | [CVPR2024, Highlight] Official code for DragDiffusion | [
"general-ml",
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T19:54:32Z | false | 95 | 663,801,546 | https://yujun-shi.github.io/projects/dragdiffusion.html | Python | Apache-2.0 | [
"artificial-intelligence",
"diffusion"
] | Yujun-Shi/DragDiffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T19:54:32Z | [
"2306.14435"
] | 2024-01-29T13:25:43Z | [
"general.topic-artificial-intelligence"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1,257 | [
"artificial-intelligence",
"cvpr2024",
"diffusion-models",
"dragdiffusion",
"draggan",
"image-editing"
] | 2026-09-22T05:42:24Z | https://github.com/Yujun-Shi/DragDiffusion | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-09T22:42:20Z | Official PyTorch Implementation of Our Paper Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks | [
"reinforcement-learning",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 664,411,752 | https://sites.google.com/aucklanduni.ac.nz/nasa-td3-pytorch/home | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | UoA-CARES/NaSA_TD3 | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2024-06-05T05:21:53Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [
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] | 2025-11-09T05:32:50Z | https://github.com/UoA-CARES/NaSA_TD3 | null |
[
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"speech"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-speech-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-10T04:51:28Z | Code for paper "Noise-aware Speech Enhancement using Diffusion Probabilistic Model" | [
"audio",
"generative-modeling",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 3 | 664,484,558 | null | Python | MIT | [
"diffusion",
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"representation"
] | YUCHEN005/NASE | [
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"query-match",
"repository-metadata"
] | 3 | 2026-10-06T17:10:23Z | [] | 2024-06-10T05:44:36Z | [
"audio-speech-enhancement"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 90 | [] | 2026-10-01T02:16:01Z | https://github.com/YUCHEN005/NASE | null |
[
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] | [
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-10T11:34:12Z | Code for our paper "Evaluate AMR Graph Similarity via Self-supervised Learning" in ACL 2023 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 664,620,567 | null | Python | null | [
"representation-learning",
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] | zzshou/AMRSim | [
"description",
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] | 2 | 2026-10-05T19:54:32Z | [] | 2024-01-16T12:53:34Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-03-29T12:03:45Z | https://github.com/zzshou/AMRSim | null |
[
"computer-vision",
"deep-learning",
"health-and-biomedicine",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-11T03:02:38Z | This is the official code of MICCAI23 paper "Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 8 | 664,910,090 | null | Python | null | [
"contrastive-learning",
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"self-supervised-learning"
] | CSCYQJ/MICCAI23-ProtoContra-SFDA | [
"description",
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] | 6 | 2026-10-22T16:09:07Z | [] | 2023-09-27T15:10:19Z | [
"general.contrastive-learning",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 38 | [] | 2026-10-13T12:15:56Z | https://github.com/CSCYQJ/MICCAI23-ProtoContra-SFDA | null |
[
"generative-ai",
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] | [
"diffusion"
] | [
"description",
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"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-11T05:05:14Z | New methods to mitigate frequency biases in diffusion models | [
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"generative-modeling"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 664,940,332 | null | Python | WTFPL | [
"diffusion"
] | rsn870/spectral_ddpm | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2023-10-31T04:05:23Z | [
"llm.diffusion-phrase"
] | d39de392cb03f50a8fb1c8353c262230be4e021f | 2026-10-07T19:34:31Z | gh-ml-readme-evidence-v2 | "d39de392cb03f50a8fb1c8353c262230be4e021f" | [
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] | [
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] | ok | 2026-10-07T19:34:31Z | rsn870/spectral_ddpm | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
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] | include | ml-contribution-v5 | 0 | [] | 2023-07-11T05:06:47Z | https://github.com/rsn870/spectral_ddpm | null |
[
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] | [
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] | [
"description",
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] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-12T02:22:49Z | Official code for the paper "Evolving Graph Transformers for Estimating Rule of Law Government". | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 665,345,220 | null | Python | null | [
"graph-transformer",
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] | YuxinTang0315/PATAS | [
"description",
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"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-07-12T11:14:03Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-07-12T02:31:57Z | https://github.com/YuxinTang0315/PATAS | null |
[
"generative-ai",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-12T10:38:57Z | Code for paper DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 2 | 665,498,770 | null | Jupyter Notebook | Apache-2.0 | [
"diffusion"
] | hexiaoxiao-cs/DMCVR | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-01-12T00:16:03Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 14 | [] | 2026-06-28T03:59:21Z | https://github.com/hexiaoxiao-cs/DMCVR | null |
[] | [
"transformer"
] | [
"description",
"license-metadata",
"paper-reference",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-12T11:32:03Z | [ICCV2023] This is an official implementation for "Scale-Aware Modulation Meet Transformer". | [] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T18:33:55Z | false | 15 | 665,517,244 | https://arxiv.org/abs/2307.08579 | Python | MIT | [
"transformer"
] | AFeng-x/SMT | [
"description",
"license-metadata",
"paper-reference",
"repository-metadata"
] | 1 | 2026-10-06T18:33:55Z | [
"2307.08579"
] | 2023-08-01T13:47:59Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 216 | [] | 2026-09-12T18:16:00Z | https://github.com/AFeng-x/SMT | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"computer-vision",
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] | [
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"metric-learning"
] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-12T14:45:45Z | Implementation of our paper, 'Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval.' | [
"computer-vision",
"information-retrieval"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 4 | 665,593,805 | null | Python | GPL-3.0 | [
"image-retrieval",
"metric-learning"
] | LuminosityX/HAT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2023-12-03T13:39:07Z | [
"vision.image-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 27 | [] | 2026-05-10T05:44:55Z | https://github.com/LuminosityX/HAT | null |
[
"computer-vision"
] | [
"detection"
] | [
"description",
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"vision.object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-13T11:33:05Z | [CVPR 2023] Official implementation of the paper "Semi-DETR: Semi-Supervised Object Detection with Detection Transformers" | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:44:14Z | false | 16 | 665,957,664 | https://arxiv.org/abs/2307.08095 | Python | MIT | [
"detection"
] | JCZ404/Semi-DETR | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:44:14Z | [] | 2024-11-26T12:51:39Z | [
"vision.object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 117 | [] | 2026-09-27T05:47:42Z | https://github.com/JCZ404/Semi-DETR | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
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"github-topics",
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"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-13T14:24:45Z | Official implementation of the paper DiffDefence: defending against adversarial attacks via diffusion models. ICIAP 2023. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 2 | 666,023,015 | null | Python | null | [
"diffusion"
] | HondamunigePrasannaSilva/DiffDefence | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-02-01T17:43:24Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 20 | [
"adversarial-attacks",
"adversarial-defence",
"diffusion-models"
] | 2025-12-16T12:33:58Z | https://github.com/HondamunigePrasannaSilva/DiffDefence | null |
[
"forecasting",
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"time-series",
"time-series-and-forecasting",
"transportation"
] | [
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] | [
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"repository-metadata"
] | [
"probable.scientific-surrogate-modeling",
"timeseries.traffic-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-14T12:57:49Z | The official implementation of the paper: "Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation". | [
"scientific-computing",
"time-series-and-forecasting"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 666,411,418 | null | Python | MIT | [
"scientific-machine-learning",
"surrogate-modeling"
] | XiongxiaoXu/ML4InterconectNetwork | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-12T20:31:19Z | [] | 2024-08-23T04:02:00Z | [
"probable.scientific-surrogate-modeling"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
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] | include | ml-contribution-v5 | 5 | [] | 2026-02-13T17:52:08Z | https://github.com/XiongxiaoXu/ML4InterconectNetwork | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal-learning"
] | [
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] | [
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"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-15T06:09:31Z | Official implementation of SEED-LLaMA (ICLR 2024). | [
"computer-vision",
"generative-ai",
"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 33 | 666,655,722 | https://ailab-cvc.github.io/seed | Python | NOASSERTION | [
"fine-tuning",
"instruction-tuning"
] | AILab-CVC/SEED | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-04T15:58:58Z | [
"2307.08041"
] | 2024-09-21T15:13:36Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 641 | [
"foundation-model",
"multimodal",
"vision-language"
] | 2026-09-04T07:57:20Z | https://github.com/AILab-CVC/SEED | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-17T03:44:16Z | This is the official code for the paper "Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking". | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 667,234,578 | null | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | lyfeng001/MLKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2024-10-20T14:41:36Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-05-04T10:52:09Z | https://github.com/lyfeng001/MLKD | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"flow-matching",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-17T05:31:48Z | Official PyTorch implementation of the paper: Flow Matching in Latent Space | [
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 23 | 667,259,013 | https://vinairesearch.github.io/LFM/ | Python | AGPL-3.0 | [
"flow-matching"
] | VinAIResearch/LFM | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 4 | 2026-10-02T18:08:49.004055Z | [] | 2025-01-20T14:57:43Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 365 | [
"flow-matching"
] | 2026-09-27T17:46:01Z | https://github.com/VinAIResearch/LFM | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["flow-matching"]} |
[
"data-centric-ai",
"distributed-ml",
"language",
"privacy-and-federated-learning"
] | [
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"language-model",
"synthetic-data"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.synthetic-data",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-17T11:09:07Z | Official implementation of the paper "Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 2 | 667,374,689 | null | Python | MIT | [
"collaborative-learning",
"federated-learning"
] | lyn1874/fedssyn | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-08T18:15:14Z | [] | 2023-07-17T11:14:12Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [
"federatedlearning",
"machinelearning",
"optimization"
] | 2024-09-20T09:24:08Z | https://github.com/lyn1874/fedssyn | null |
[
"generative-ai",
"generative-modeling",
"machine-learning",
"reliable-ml",
"robust-ml",
"trustworthy-ml"
] | [
"diffusion",
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"uncertainty-estimation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"probable.evaluation-ood-readme",
"trust.out-of-distribution-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T03:00:43Z | [ICCV 2023] The official implementation of paper "DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models" | [
"generative-modeling",
"machine-learning",
"reliable-ml"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 1 | 667,660,275 | null | Python | null | [
"diffusion",
"out-of-distribution-detection",
"uncertainty-estimation"
] | cure-lab/DiffGuard | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2024-01-08T06:10:48Z | [
"probable.evaluation-ood-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [
"deep-learning",
"diffusion-models",
"iccv2023",
"ood-detection",
"pytorch"
] | 2026-02-13T15:50:31Z | https://github.com/cure-lab/DiffGuard | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"multimodal",
"multimodal-learning"
] | [
"diffusion",
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"multimodal-learning",
"vision-language"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"medical.medical-imaging",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T05:20:39Z | [ICCV 2023 Oral] Official implementation for "DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion." | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"multimodal-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:44:14Z | false | 27 | 667,693,735 | null | Python | null | [
"diffusion",
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] | Zhaozixiang1228/MMIF-DDFM | [
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] | 2 | 2026-10-04T15:58:58Z | [] | 2026-06-08T15:51:08Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 283 | [
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] | 2026-10-03T03:48:51Z | https://github.com/Zhaozixiang1228/MMIF-DDFM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T07:09:59Z | The official PyTorch implementation of the IEEE/CVF International Conference on Computer Vision (ICCV) '23 paper Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 11 | 667,728,144 | https://openaccess.thecvf.com/content/ICCV2023/html/Flaborea_Multimodal_Motion_Conditioned_Diffusion_Model_for_Skeleton-based_Video_Anomaly_Detection_ICCV_2023_paper.html | Jupyter Notebook | MIT | [
"diffusion"
] | aleflabo/MoCoDAD | [
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] | 1 | 2026-10-05T20:10:04Z | [] | 2026-05-17T21:33:36Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 92 | [
"anomaly-detection",
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] | 2026-09-27T13:28:02Z | https://github.com/aleflabo/MoCoDAD | null |
[
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] | [
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] | [
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] | [
"medical.medical-image-segmentation",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T08:49:48Z | This is the official code of the paper "Few Shot Medical Image Segmentation with Cross Attention Transformer" | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 7 | 667,764,232 | null | Python | Apache-2.0 | [
"segmentation",
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] | hust-linyi/CAT-Net | [
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] | 4 | 2026-10-22T16:09:07Z | [] | 2023-09-27T10:02:59Z | [
"vision.segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 66 | [] | 2026-05-11T03:56:55Z | https://github.com/hust-linyi/CAT-Net | null |
[
"deep-learning",
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] | [
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] | [
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"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T09:28:32Z | Code for paper: Knowledge-Guided Semantically Consistent Contrastive Learning for Sequential Recommendation. | [
"deep-learning",
"recommender-systems",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 667,778,783 | null | null | null | [
"contrastive-learning",
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] | Lightblues/KGSCL | [
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] | 1 | 2026-10-23T15:59:01Z | [] | 2023-07-07T07:55:17Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-07-18T09:28:32Z | https://github.com/Lightblues/KGSCL | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T18:05:37Z | Generate New Pokemons using Diffusion Models | [
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 667,972,066 | null | Python | MIT | [
"diffusion"
] | rajatvisitme/PokeFusion | [
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"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2023-07-22T18:02:53Z | [
"llm.diffusion-phrase"
] | ea0d3f3689f395e645392dced797ccd89b756da8 | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "ea0d3f3689f395e645392dced797ccd89b756da8" | [
"dataset",
"method",
"other"
] | [
"ml-method-context"
] | ok | 2026-10-06T18:32:27Z | rajatvisitme/PokeFusion | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
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"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-07-21T18:11:40Z | https://github.com/rajatvisitme/PokeFusion | null |
[
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] | [
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] | [
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] | [
"llm.language-model",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T20:56:03Z | Official code implemtation of paper AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos? | [
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"natural-language-processing",
"time-series-and-forecasting"
] | [
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"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 3 | 668,023,671 | https://brown-palm.github.io/AntGPT/ | Python | MIT | [
"language-model"
] | brown-palm/AntGPT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-11-01T17:09:41Z | [
"2307.16368"
] | 2024-09-23T17:42:15Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 30 | [
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"vision-and-language"
] | 2026-10-02T02:09:39Z | https://github.com/brown-palm/AntGPT | null |
[
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] | [
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] | [
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] | [
"general.gradient-boosting",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-18T21:32:10Z | A Novel Stacked Ensemble Model of Random Forest, Gradient Boosting & Bagging Regressor with RNN as the meta-learner layer for Crop Yield Prediction | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 668,032,511 | null | Jupyter Notebook | null | [
"ensemble-learning",
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] | HarshGupta-2002/Crop-Yield-Prediction-System | [
"description",
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"query-match",
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] | 2 | 2026-10-12T20:31:19Z | [] | 2024-04-23T12:42:46Z | [
"general.gradient-boosting"
] | 5c7a8071ea4f466d2b9e1fced18f26c8cfb1eadf | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "5c7a8071ea4f466d2b9e1fced18f26c8cfb1eadf" | [
"other"
] | [
"method-contribution",
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] | ok | 2026-10-02T18:21:54Z | HarshGupta-2002/Crop-Yield-Prediction-System | specific-method-with-novelty-claim | [
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"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [
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"data-visualization",
"deep-learning",
"flask-application",
"machine-learning",
"python",
"stacking-ensemble"
] | 2025-12-19T10:33:05Z | https://github.com/HarshGupta-2002/Crop-Yield-Prediction-System | null |
[
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"healthcare",
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] | [
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"transform... | [
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] | [
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"specialized26.noisy-label... | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-19T01:04:39Z | [MICCAI 2023 Oral] The official code of "Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction" (top 9%) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 15 | 668,072,997 | null | Python | null | [
"genomics",
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] | Cassie07/PathOmics | [
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] | 19 | 2026-11-01T17:09:41Z | [] | 2025-12-16T20:53:56Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 121 | [] | 2026-09-22T03:21:23Z | https://github.com/Cassie07/PathOmics | null |
[
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"graph-learning"
] | [
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] | [
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-19T07:17:00Z | Code for paper 'Node-oriented Spectral Filtering for Graph Neural Networks'（TPAMI2024） | [
"deep-learning",
"graph-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 668,166,117 | null | Python | null | [
"graph-neural-network",
"message-passing",
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] | SsGood/NFGNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2024-03-11T05:30:24Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 15 | [] | 2026-04-09T06:50:25Z | https://github.com/SsGood/NFGNN | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-19T08:31:35Z | The official code of our ICCV2023 work: Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation | [
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"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 23 | 668,191,136 | null | Python | null | [
"generative-modeling",
"video-generation"
] | harlanhong/ICCV2023-MCNET | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2023-10-05T18:43:07Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 252 | [
"animation",
"deepfake",
"face",
"gan",
"image-generation",
"motion-transfer",
"reenactment",
"talking-face-generation",
"talking-head"
] | 2026-07-22T10:04:51Z | https://github.com/harlanhong/ICCV2023-MCNET | null |
[
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [
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"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-19T19:36:26Z | This repository contains the source code associated with our paper titled "Bidirectional Mapping with Contrastive Learning on Multimodal Neuroimaging Data," which has been accepted at MICCAI 2023. | [
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 668,429,621 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | FlynnYe/BMCL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2024-06-30T07:03:58Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2026-06-18T09:04:14Z | https://github.com/FlynnYe/BMCL | null |
[
"deep-learning",
"representation-learning",
"robotics-and-control"
] | [
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"distillation",
"lifelong-learning"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"general.continual-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-19T20:30:56Z | Official implementation of “Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery”, MICCAI 2023 | [
"deep-learning",
"representation-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 668,444,800 | null | Python | MIT | [
"continual-learning",
"distillation",
"lifelong-learning"
] | longbai1006/CS-VQLA | [
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"query-match",
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] | 1 | 2026-10-12T20:31:19Z | [] | 2024-07-07T14:05:15Z | [
"general.continual-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 8 | [] | 2026-06-08T16:47:15Z | https://github.com/longbai1006/CS-VQLA | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization"
] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-19T21:20:23Z | Official code for the paper "Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach" | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 668,457,250 | null | Python | MIT | [
"policy-gradient",
"policy-optimization"
] | xubo92/async-mahrl | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2023-11-13T00:29:39Z | [
"rl.policy-gradient"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-03-19T11:51:44Z | https://github.com/xubo92/async-mahrl | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"vision"
] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
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"llm.diffusion-phrase",
"vision.image-restoration",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-20T09:19:57Z | This project is the official implementation of 'Diffir: Efficient diffusion model for image restoration', ICCV2023 | [
"computer-vision",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 27 | 668,640,050 | null | Jupyter Notebook | Apache-2.0 | [
"diffusion",
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] | Zj-BinXia/DiffIR | [
"description",
"github-topics",
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] | 17 | 2026-10-19T17:46:36Z | [] | 2024-08-25T18:13:37Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 613 | [
"deblurring",
"diffusion-model",
"inpainting",
"super-resolution"
] | 2026-10-12T03:45:36Z | https://github.com/Zj-BinXia/DiffIR | null |
[
"deep-learning",
"graph-learning",
"natural-language-processing"
] | [
"graph-neural-network",
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] | [
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"repository-metadata"
] | [
"graph.gnn-description",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-20T11:28:30Z | This repository contains the official implementation of the paper titled "Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network", which was presented in the 18th International Conference on Document Analysis and Recognition (ICDAR 2024). | [
"deep-learning",
"graph-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 668,688,654 | https://doi.org/10.1007/978-3-031-70552-6_15 | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | HamzaGbada/direct-neighbor-vrd | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2024-09-14T16:19:40Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [
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"gcn",
"gnn-model",
"graph-convolutional-networks",
"graph-neural-networks",
"icdar2024",
"pytorch"
] | 2026-10-09T18:28:32Z | https://github.com/HamzaGbada/direct-neighbor-vrd | null |
[
"3d",
"computer-vision",
"graphics"
] | [
"neural-rendering",
"radiance-fields",
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] | [
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] | [
"multimodal.nerf"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-20T12:20:56Z | Official code for ICCV 2023 paper: "TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering". | [
"3d",
"computer-vision",
"graphics"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 2 | 668,708,377 | null | Python | NOASSERTION | [
"neural-rendering",
"radiance-fields",
"transformer"
] | pansanity666/TransHuman | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2024-01-11T07:34:39Z | [
"multimodal.nerf"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 67 | [
"3dreconstruction",
"avatar",
"nerf",
"transformer"
] | 2025-09-08T12:03:33Z | https://github.com/pansanity666/TransHuman | null |
[
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing",
"optimization"
] | [
"diffusion",
"fine-tuning",
"instruction-tuning",
"language-model",
"pretraining",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-20T13:28:45Z | [ACL 2023] The official implementation of "CAME: Confidence-guided Adaptive Memory Optimization" | [
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing",
"optimization"
] | [
"deep-learning",
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 9 | 668,735,812 | null | Python | MIT | [
"diffusion",
"fine-tuning",
"instruction-tuning",
"transformer"
] | yangluo7/CAME | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2025-03-22T08:39:50Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 100 | [
"deep-learning",
"diffusion-transformer",
"large-language-models",
"memory-efficient",
"optimizer"
] | 2026-07-13T14:47:02Z | https://github.com/yangluo7/CAME | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-20T16:11:33Z | the official code of paper "DiffDP: Radiotherapy Dose Prediction via a Diffusion Model" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 668,802,487 | null | null | null | [
"diffusion"
] | scufzh/DiffDP | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2023-07-20T17:39:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2025-04-25T13:06:38Z | https://github.com/scufzh/DiffDP | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-20T16:40:00Z | Official Pytorch Implementation for "TokenFlow: Consistent Diffusion Features for Consistent Video Editing" presenting "TokenFlow" (ICLR 2024) | [
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 143 | 668,812,621 | https://diffusion-tokenflow.github.io | Python | MIT | [
"diffusion"
] | omerbt/TokenFlow | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-04T15:44:14Z | [
"2307.10373"
] | 2025-02-03T15:34:18Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1,707 | [
"iclr2024",
"stable-diffusion",
"text-to-image",
"text-to-video",
"tokenflow",
"video-editing"
] | 2026-09-28T03:57:30Z | https://github.com/omerbt/TokenFlow | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-21T08:53:49Z | Official implementation of the paper "Enhancing Low Resolution Face Recognition with Feature Similarity Knowledge Distillation"" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 4 | 669,062,879 | null | null | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | kangkzeng/feature-similarity-KD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-04-25T15:20:26Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-07-14T04:59:18Z | https://github.com/kangkzeng/feature-similarity-KD | null |
[
"generative-ai",
"generative-modeling",
"vision"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-07-23T03:54:45Z | [ICCV 2023] "TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition" (Official Implementation) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 100 | 669,671,669 | https://shilin-lu.github.io/tf-icon.github.io/ | Python | MIT | [
"diffusion"
] | Shilin-LU/TF-ICON | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-04T15:44:14Z | [] | 2025-03-06T04:05:39Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 814 | [
"diffusion-model",
"generative-ai",
"image-composition",
"image-inversion",
"stable-diffusion",
"text-to-image"
] | 2026-10-04T00:59:21Z | https://github.com/Shilin-LU/TF-ICON | null |
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