repo stringlengths 8 116 | tasks stringlengths 8 117 | titles stringlengths 17 302 | dependencies stringlengths 5 372k | readme stringlengths 5 4.26k | __index_level_0__ int64 0 4.36k |
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AngeLouCN/DC-UNet | ['medical image segmentation', 'semantic segmentation'] | ['DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation'] | main.py model.py saveModel evaluateModel tversky_loss jacard iou_loss dice_coef_loss focal_tversky dice_coef tversky trainStep trans_conv2d_bn DCUNet conv2d_bn ResPath DCBlock flatten sum flatten sum flatten sum tversky makedirs write save to_json open saveModel round open subplot str imshow title savefig sum range pre... | # DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation <div align=center><img src="https://github.com/AngeLouCN/DC-UNet/blob/main/results/result.PNG" width="784" height="462" alt="Result"/></div> This repository contains the implementation of a new version U-Net (DC... | 100 |
AngryCai/BS-Nets | ['hyperspectral image classification'] | ['BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image'] | BS_Net_FC.py utility.py Preprocessing.py BS_Net_Conv.py Helper.py __init__.py BS_Net_Conv BS_Net_FC Dataset cal_mean_spectral_angle eval_band_cv cal_mean_spectral_divergence eval_band maxabs_scale accuracy_score Processor KNN predict fit save_res_4kfolds_cv len Processor append train_test_split range predict fit asarra... | # BS-Nets # Band selection (feature selection) of hyperspectral image using neural networks with attention mechanism. **Requirement** - TensorFlow-1.6 Please cite the following paper. > > [Y. Cai, X. Liu, and Z. Cai, "BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image," IEEE Transactions on G... | 101 |
Animadversio/FloodFillNetwork-Notes | ['boundary detection', 'semantic segmentation'] | ['Flood-Filling Networks'] | ffn/training/augmentation.py compute_partitions.py agglomeration_graph_gen.py ffn/utils/ortho_plane_visualization.py analysis_script/visualize_seed.py analysis_script/visualize_segmentation.py ffn/inference/align.py ffn/inference/inference_flags.py ffn/inference/executor.py ffn/inference/resegmentation.py ffn/training/... | # Flood-Filling Networks Flood-Filling Networks (FFNs) are a class of neural networks designed for instance segmentation of complex and large shapes, particularly in volume EM datasets of brain tissue. For more details, see the related publications: * https://arxiv.org/abs/1611.00421 * https://doi.org/10.1101/200675 ... | 102 |
AnirudhMukherjee/story-generation | ['story generation'] | ['Hierarchical Neural Story Generation'] | storygeneration/beam.py storygeneration/tests/test_example.py storygeneration/model.py storygeneration/multipara.py storygeneration/utils.py storygeneration/sample.py storygeneration/tests/test_beam.py storygeneration/sentiment.py flask_storygeneration.py storygeneration/train.py storygeneration/tests/test_train.py sto... | # Hierarchical Story Generation ## Abstract <p>Story generation involves developing a system that can write stories in a manner such that the similarity between the story written by the system is close to stories written by a human. The story generation system that we are working on generates a well-structured, coheren... | 103 |
Anna996/Neural-Style-Transfer-Project | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | project.py style_layers get_loss_and_grads deprocess_image style_loss_of_layer content_loss style_loss gram_matrices_of_style_image astype clip sum function square style_layers function transpose matmul batch_flatten permute_dimensions append transpose square matmul batch_flatten shape permute_dimensions sum style_laye... | # Neural-Style-Transfer-Project The project is based on Deep Neural Networks, which creates artistic images by learning the content of one image and the style of the other image. The project was written in Python in Colab (by Google), using TensorFlow and VGG16 - a convolutional neural network model. ![res5](https://us... | 104 |
Annbless/DUTCode | ['video stabilization'] | ['DUT: Learning Video Stabilization by Simply Watching Unstable Videos'] | utils/image_utils.py models/DIFRINT/flowlib.py scripts/DUTStabilizer.py utils/IterativeSmooth.py scripts/StabNetStabilizer.py models/DIFRINT/models.py models/StabNet/v2_93.py models/StabNet/model.py models/DUT/MotionPro.py models/correlation/correlation.py models/StabNet/pytorch_resnet_v2_50.py models/DUT/DUT.py config... | <h1 align="left">DUT: Learning Video Stabilization by Simply Watching Unstable Videos <a href="https://arxiv.org/pdf/2011.14574.pdf"><img src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg" ></a> <a href="https://colab.research.google.com/drive/1i-cK-6uFKbWRjxF26uxUqHHSEvq1Ln8h?usp=sharing"> <img src="https:/... | 105 |
AnonymousDLMA/MI_with_DA | ['data augmentation'] | ['How Does Data Augmentation Affect Privacy in Machine Learning?'] | models/resnet_cifar.py models/twolayer_convnet.py mi_attack.py models/wrn16_8.py models/__init__.py mi_utils.py custom_augs.py utils.py cifar_train.py test adjust_learning_rate train_vs_test train checkpoint train_classifier verify_unlearning trn_or_test load_all_stat load_array Net get_ground_truth test_classifier sof... | # Code for Neurips2020 submission "Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary" ## Dependency This code is tested with [torch 1.5](https://github.com/pytorch/pytorch) and [numpy 1.14](https://numpy.org/). We use benchmark datasets [CIFAR10 and CIFAR100](https://... | 106 |
Anou9531/Laplacian | ['graph learning'] | ['Learning Laplacian Matrix in Smooth Graph Signal Representations'] | code/main.py code/data_loader.py synthetic_data_gen get_a_vec get_precision_rnd get_precision_er_L get_MSE create_dup_matrix create_A_mat create_b_mat get_precision_er create_static_matrices_for_L_opt get_T_mat create_G_mat get_prec_recall_rnd_L get_f_score get_recall_er_L gl_sig_model get_u_vec T norm ones reshape squ... | # Learn-Graph-Laplacian This is an implementation of the paper Learning Laplacian Matrix in Smooth Graph Signal Representations https://arxiv.org/pdf/1406.7842.pdf The original code can be found on the authors website [web.media.mit.edu/~xdong/pub.html](https://web.media.mit.edu/~xdong/pub.html) # Tests - Precision, ... | 107 |
AntonioCarta/mslmn | ['speech recognition'] | ['Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory'] | mslmn/cannon/tasks/__init__.py mslmn/incremental_train.py mslmn/iamondb.py mslmn/cannon/utils.py mslmn/cannon/model_selection.py mslmn/cannon/regularizers.py mslmn/models.py mslmn/cannon/laes/__init__.py mslmn/cannon/tasks/interface.py mslmn/task_trainer.py mslmn/cannon/laes/big_svd.py mslmn/cannon/laes/svd_la.py mslmn... | # Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory MSLMN code for IAM-OnDB experiments and incremental training. This codebase implements our recurrent model based on a hierarchical recurrent neural network architecture. The model is trained incrementally by dynamically expandi... | 108 |
Anunay1234/Sentiment-Analysis-using-LSTM | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | imdbReviews.py imdb.py imdb_bidirectional_lstm.py load_data extract_words build_dict load_data main grab_data seed load endswith close shuffle array zip append max open join rstrip replace words strip translate lower sub maketrans split append len list extract_words chdir getcwd print glob len dict sum keys enumerate v... | # Sentiment-Analysis-using-LSTM # LSTM-sentiment-analysis Due to computationly intensive of LSTM method, we only use two LSTM layes in our classifcation model. These two LSTM layes are bidirectional, which include a forwads LSTM and a backwards LSTM. Feature extraction was done by reading all training reviews and toke... | 109 |
ApGa/adversarial_deepfakes | ['face swapping'] | ['Adversarial Perturbations Fool Deepfake Detectors'] | utils/parallelized_classifier.py adv_examples.py evaluation.py dip_template.py utils/crop.py utils/HHReLU.py classifier.py cw_attack.py generate_dataset.py utils/dip_utils.py scaled_bim test_softmax_batch bim test test_softmax cw_greedy_round ImageFolderWithPaths fgsm carlini save_batch generate_adversarial_examples if... | # adversarial_deepfakes Deepfakes with an adversarial twist. This repository provides code and additional materials for the paper: "Adversarial perturbations fool deepfake detectors", Apurva Gandhi and Shomik Jain, To Appear in IJCNN 2020. The paper uses adversarial perturbations to enhance deepfake images and fool com... | 110 |
AppleHolic/source_separation | ['speech enhancement'] | ['Phase-aware Speech Enhancement with Deep Complex U-Net'] | source_separation/settings.py source_separation/train_jointly.py source_separation/synthesize.py source_separation/train.py setup.py source_separation/modules.py source_separation/hyperopt_run.py source_separation/dataset.py source_separation/__init__.py source_separation/models.py source_separation/trainer.py get_requ... | # Source Separation [](https://www.python.org/downloads/release/python-360/) [](https://hits.seeyoufarm.com) [![Synthesis Example On C... | 111 |
ArantxaCasanova/ralis | ['active learning', 'semantic segmentation'] | ['Reinforced active learning for image segmentation'] | utils/logger.py data/gtav.py data/data_utils.py utils/replay_buffer.py data/camvid_al.py models/fpn_bayesian.py run.py models/model_utils.py utils/transforms.py train_supervised.py utils/progressbar.py utils/parser.py utils/joint_transforms.py data/cityscapes.py data/cityscapes_al_splits.py data/cityscapes_al.py models... | # Reinforced Active Learning for Image Segmentation (RALIS) Code for the paper [Reinforced Active Learning for Image Segmentation](https://arxiv.org/abs/2002.06583) ## Dependencies - python 3.6.5 - numpy 1.14.5 - scipy 1.1.0 - Pytorch 0.4.0 ## Scripts The folder 'scripts' contains the different bash scripts that could... | 112 |
ArashRabbani/DeePore | ['physical simulations'] | ['DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials'] | Demo1.py Demo2.py Demo5.py Demo3.py DeePore.py Demo4.py DeePore7 check_get gener ecl_distance DeePore6 DeePore2 WMSE splitdata WBCE parfor loadmodel create_compact_dataset writeh5slice show_feature_maps makeblocks hdf_shapes modelmake normalize DeePore3 showentry predict shuf normal DeePore5 testmodel DeePore8 DeePore4... | # DeePore: Deep learning for rapid characterization of porous materials ## Summary DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro–tomography images. By combining naturally occurring porous textures we generated 17,700 semi–real 3–D mi... | 113 |
ArashRahnama/Adversarial-Explainations-for-Artificial-Intelligence-systems-AXAI | ['speech recognition', 'adversarial attack'] | ['An Adversarial Approach for Explaining the Predictions of Deep Neural Networks'] | AXAI.py AXAI | # Adversarial-Explanations-for-Artificial-Intelligence-Systems-AXAI This is the codebase for our AXAI explainability algorithm from our paper "An Adversarial Approach for Explaining the Predictions of Deep Neural Networks" under review for NeurIPS 2020 and available @https://arxiv.org/abs/2005.10284. | 114 |
ArashRahnama/Adversarial-Explanations-for-Artificial-Intelligence-Systems-AXAI | ['speech recognition', 'adversarial attack'] | ['An Adversarial Approach for Explaining the Predictions of Deep Neural Networks'] | AXAI.py AXAI | # Adversarial-Explanations-for-Artificial-Intelligence-Systems-AXAI This is the codebase for our AXAI explainability algorithm from our paper "An Adversarial Approach for Explaining the Predictions of Deep Neural Networks" under review for NeurIPS 2020 and available @https://arxiv.org/abs/2005.10284. | 115 |
Ardibid/ArtisticStyleRoboticPainting | ['style transfer'] | ['Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists'] | python_files/vae_models.py python_files/vaes_encoder.py python_files/vae_main.py python_files/vae_loss.py python_files/vae_recons_interps.py python_files/vae_plots.py python_files/vae_dataset.py python_files/vaes_generators.py python_files/vae_train.py Net Encoder_MLP Encoder Reshape Net Generator_MLP Generator Reshape... | # Artistic Style Robotic Painting by: [Ardavan Bidgoli](ardavan.io), [Manuel Rodriguez Ladrón de Guevara](https://github.com/manuelladron) , [Cinnie Hsiung](https://github.com/cinniehsiung?tab=overview&from=2017-01-01&to=2017-01-31), [Jean Oh](https://github.com/jeanoh) , [Eunsu Kang](https://github.com/kangeunsu) ###... | 116 |
Arnaud15/CS236_Neural_Processes_For_Image_Completion | ['gaussian processes'] | ['Conditional Neural Processes'] | NP.py test.py models.py utils.py NP_CIFAR10.py VectorAttentionAggregator ContextEncoder Decoder ContextToLatentDistributionCIFAR QueryAttentionAggregator ContextToLatentDistribution DecoderCIFAR ContextEncoderCIFAR MeanAgregator main train all_forward compute_loss main train all_forward compute_loss main log_normal sav... | ### CS236 Deep Generative Processes ## Neural Processes for Image Completion #### Amaury Sabran, Arnaud Autef, Benjamin Petit In this project, we develop image completion techniques based on Neural Processes , a recently proposed class of models that uses neural networks to describe distributions over functions. We sho... | 117 |
Artaches/SSAN-self-attention-sentiment-analysis-classification | ['sentiment analysis'] | ['Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers'] | Utils/Datasets.py Utils/Representations.py ave.py bow.py cnn.py joint.py Utils/MyMetrics.py lstm_bilstm.py retrofit.py Utils/twokenize.py Utils/WordVecs.py Utils/Semeval_2013_Dataset.py Utils/SenTube_Dataset.py Utils/emoticons.py san.py get_best_C print_prediction print_results test_embeddings main test print_predictio... | # SSAN-self-attention-sentiment-analysis-classification Code for the paper "Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers": http://aclweb.org/anthology/W18-6219, https://arxiv.org/abs/1812.07860 . This paper was published in WASSA 2018 (9th Workshop on Computational Approache... | 118 |
Au3C2/GVS | ['style transfer', 'anomaly detection'] | ['Generator Versus Segmentor: Pseudo-healthy Synthesis'] | unet/__init__.py utils/split_cases_lits.py unet/unet_model.py utils/ms_ssim.py utils/nii2npy_brats.py unet/unet_parts.py utils/dataset.py unet/networks.py test.py utils/nii2npy_lits.py utils/init_logging.py utils/split_cases_brats.py main.py train_net get_args get_args predict get_norm_layer PixelDiscriminator Identity... | ## 📝 Table of Contents - [About](#about) - [Getting Started](#getting_started) - [Usage](#usage) - [Authors](#authors) - [Acknowledgments](#acknowledgement) ## 🧐 About <a name = "about"></a> This is the anonymous code of GVS, which mainly includes training details, pretrained model and the synthetic images of one vol... | 119 |
AustinDoolittle/Pytorch-Gain | ['object localization', 'semantic segmentation'] | ['Tell Me Where to Look: Guided Attention Inference Network'] | gain.py data.py models.py transform.py main.py mx_to_cv write_image cv_to_mx scale_to_range ImageDataset RawDataset load_image AttentionGAIN tile_images scalar train_handler infer_handler set_available_gpus model_info_handler parse_args ResidualBlock Darknet53 model_to_str get_model GreyNet19 Affine Translate DropoutAn... | # Pytorch-Gain An implementation of GAIN heatmap network in pytorch. Original paper: https://arxiv.org/abs/1802.10171 | 120 |
AutoML-4Paradigm/S2E | ['automl'] | ['Searching to Exploit Memorization Effect in Learning from Corrupted Labels'] | space/co_mnist_main.py space/rbf_main.py space/co_main.py space/sin_100_main.py space/co_100_main.py heng_100_main.py alg/loss.py alg/model.py space/random_mnist_main.py alg/grad_main.py loss.py random_100_main.py random_mnist_main.py alg/band_main.py space/random_100_main.py space/rbf_mnist_main.py random_main.py heng... | # S2E ICML'20: Searching to Exploit Memorization Effect in Learning from Corrupted Labels (PyTorch implementation). ======= This is the code for the paper: [Searching to Exploit Memorization Effect in Learning from Corrupted Labels](https://arxiv.org/abs/1911.02377) Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James T. ... | 121 |
Autonise-AI/Text-Recognition | ['scene text detection', 'text classification', 'instance segmentation', 'semantic segmentation'] | ['PixelLink: Detecting Scene Text via Instance Segmentation'] | src/model/generic_model.py src/loader/dete_loader.py src/Dlmodel/TrainTestR.py src/prepare_metadata/prepare_metadata.py src/prepare_metadata/meta_synth.py src/model/crnn.py src/loader/reco_loader.py src/model/model_loader.py src/prepare_metadata/meta_artificial.py src/loader/mnist.py src/prepare_metadata/meta_ic15.py s... | # Pytorch Implementation of [Pixel-LINK](https://arxiv.org/pdf/1801.01315.pdf) ## A brief abstract of your project including the problem statement and solution approach We are attempting to detect all kinds of text in the wild. The technique used for text detection is based on the paper PixelLink: Detecting Scene Text ... | 122 |
Awesome-AutoAug-Algorithms/AWS-OHL-AutoAug | ['data augmentation'] | ['Online Hyper-parameter Learning for Auto-Augmentation Strategy'] | distm/torch.py distm/__init__.py agent/reinforce.py pipeline/ohl.py aug_op/__init__.py models/wresnet.py utils/config.py utils/misc.py distm/local.py agent/ppo.py distm/base.py models/__init__.py pipeline/__init__.py pipeline/aws.py agent/base.py utils/file.py aug_op/registry.py scheduler/__init__.py utils/dist.py pipe... | # Automatic Augmentation Zoo An integration of several popular automatic augmentation methods, including OHL ([Online Hyper-Parameter Learning for Auto-Augmentation Strategy](http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_Online_Hyper-Parameter_Learning_for_Auto-Augmentation_Strategy_ICCV_2019_paper.pdf)) an... | 123 |
AyanKumarBhunia/on-the-fly-FGSBIR | ['sketch based image retrieval', 'image retrieval', 'cross modal retrieval'] | ['Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval', 'Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval'] | dataset_chairv2.py RL_Networks.py render_sketch_chairv2.py main_chairv2.py Net_Basic_V1.py Environment_SBIR.py get_ransform CreateDataset_Sketchy Environment main_train Net_Basic redraw_Quick2RGB mydrawPNG Preprocess_QuickDraw_redraw Policy backbone_network extend Train Environment clip_grad_norm_ zero_grad calculate_l... | # Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval, CVPR 2020 (Oral) **Ayan Kumar Bhunia**, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song, “Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval”, IEEE Conf. on Computer Vision and Pattern Recognition (**CVPR*... | 124 |
Ayush1651999/Stock-Price-predictor | ['time series'] | ['Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs'] | preprocessing.py main.py new_dataset append range len | # Stock-Price-predictor This is repo for the course project of the course GNR652 - Machine Learning for remote sensing, where we programmed an LSTM network for stock price prediction Some modifications possible - Denoising the price signal to remove the stochastic component using wavelet denoising method Increasing th... | 125 |
B-Yassine/Carlini-and-Wagner_InceptionV3_Imagenet | ['adversarial attack'] | ['Towards Evaluating the Robustness of Neural Networks'] | Inception_v3.py adversarial_generation.py | # C&W_InceptionV3_Imagenet Simple implementation of the C&W attack on a pre-trained Keras InceptionV3 on Imagenet To generate the adversarial image simply run: Python adversarial_generation.py To test the classification, Run: Python Inception_v3.py # Adversarial Examples Adversarial examples are inputs... | 126 |
BCV-Uniandes/ISINet | ['semantic segmentation'] | ['ISINet: An Instance-Based Approach for Surgical Instrument Segmentation'] | maskrcnn/maskrcnn_benchmark/modeling/matcher.py maskrcnn/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py maskrcnn/maskrcnn_benchmark/data/collate_batch.py maskrcnn/maskrcnn_benchmark/modeling/backbone/backbone.py maskrcnn/maskrcnn_benchmark/engine/inference.py maskrcnn/maskrcnn_benchmark/... | # ISINet This is the Pytorch implementation of [ISINet: An Instance-Based Approach for Surgical Instrument Segmentation](https://arxiv.org/abs/2007.05533) published at [MICCAI2020](https://www.miccai2020.org/en/). ## Installation Requirements: - Python >= 3.6 - Pytorch == 1.4 - numpy - scikit-image - tqdm - scipy ==... | 127 |
BCV-Uniandes/query-objseg | ['instance segmentation', 'semantic segmentation'] | ['Dynamic Multimodal Instance Segmentation guided by natural language queries'] | dmn_pytorch/models/dpn/__init__.py dmn_pytorch/visdom_display.py dmn_pytorch/train.py dmn_pytorch/utils/misc_utils.py dmn_pytorch/models/dpn/adaptive_avgmax_pool.py dmn_pytorch/models/tests/test_dmn.py dmn_pytorch/__init__.py dmn_pytorch/models/dpn/model_factory.py dmn_pytorch/utils/word_utils.py setup.py dmn_pytorch/u... | # dmn-pytorch [](./LICENSE) [](https://www.codacy.com?utm_source=github.com&utm_medium=referral&utm_content=andfoy/query-objseg&utm_campaign=Badge_Grade) <... | 128 |
BJTUJia/person_reID_DualNorm | ['person re identification'] | ['Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice'] | person_reID_DualNorm/test_grid.py person_reID_DualNorm/train.py person_reID_DualNorm/models/MobileNet_IFN.py person_reID_DualNorm/models/__init__.py person_reID_DualNorm/test_viper.py person_reID_DualNorm/models/ResNet_IFN.py person_reID_DualNorm/losses.py person_reID_DualNorm/models/ResNet_o.py person_reID_DualNorm/te... | # person_reID_DualNorm This is the pytorch implementation of our BMVC 2019 paper "Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice" The trained models are avaiable at https://pan.baidu.com/s/1gHHJBF9IgBKlWcItZemmBg coed:5000 or https://drive.google.com/open?id=1Gy96vKH60ML9fk2znnZmz5... | 129 |
BUTSpeechFIT/BrnoLM | ['text augmentation', 'speech recognition', 'data augmentation'] | ['Text Augmentation for Language Models in High Error Recognition Scenario'] | scripts/sample-from-lm.py test/test_data_pipeline/test_multistream.py scripts/oov-clustering/reference-matrix-by-word-alignment.py test/test_oov_alignment_lib.py scripts/oov-clustering/plot-det.py scripts/rescoring/score-combiner.py scripts/train/train-pero.py brnolm/investigate-ivecs.py scripts/train/train.py scripts/... | # BrnoLM A neural language modeling toolkit built on PyTorch. This is a scientific piece of code, so expect rough edges. BrnoLM has so far powered language modeling in the following papers: * Beneš et al. [Text Augmentation for Language Models in High Error Recognition Scenario](https://arxiv.org/pdf/2011.06056.pdf) * ... | 130 |
Babylonpartners/corrsim | ['word embeddings', 'semantic textual similarity'] | ['Correlations between Word Vector Sets', 'Correlation Coefficients and Semantic Textual Similarity'] | evaluation/corrset_eval.py similarity/__init__.py evaluation/conf_intervals.py evaluation/wordsim_eval.py evaluation/utils.py evaluation/infosim_eval.py senteval/engine.py senteval/__init__.py similarity/correlation.py setup.py senteval/utils.py similarity/mi.py evaluation/corrsim_eval.py similarity/baseline.py similar... | > **Note** > This repository is no longer actively maintained by Babylon Health. For further assistance, reach out to the paper authors. # CorrSim **CorrSim** is an evaluation framework and a collection of statistical similarity measures for word vectors described in Vitalii Zhelezniak, Aleksandar Savkov, April Shen, a... | 131 |
Babylonpartners/fuzzymax | ['word embeddings', 'semantic textual similarity'] | ["Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors"] | similarity/ablation.py evaluation/wmd.py senteval/__init__.py senteval/sts.py similarity/__init__.py evaluation/classical.py similarity/fuzzy.py senteval/utils.py evaluation/fuzzy_universes.py similarity/soft_card.py evaluation/constants.py evaluation/conf_intervals.py evaluation/fuzzy_eval.py evaluation/utils.py evalu... | > **Note** > This repository is no longer actively maintained by Babylon Health. For further assistance, reach out to the paper authors. # FuzzyMax FuzzyMax is an evaluation framework and a collection of fuzzy set similarity measures for word vectors described in Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Franc... | 132 |
Babylonpartners/rgat | ['graph attention'] | ['Relational Graph Attention Networks'] | rgat/ops/sparse_ops.py rgat/ops/math_ops.py rgat/layers/relational_graph_convolution.py rgat/layers/__init__.py examples/rdf/example.py examples/batching/example_eager.py examples/batching/example_static.py rgat/layers/relational_graph_attention_logits.py rgat/utils/graph_utils.py rgat/layers/basis_decomposition_dense.... | > **Note** > This repository is no longer actively maintained by Babylon Health. For further assistance, reach out to the paper authors. # Relational Graph Attention Networks A TensorFlow implementation of Relational Graph Attention Networks for semi-supervised node classification and graph classification tasks introdu... | 133 |
Baileyswu/pytorch-hmm-vae | ['speech recognition', 'noisy speech recognition', 'distant speech recognition'] | ['The PyTorch-Kaldi Speech Recognition Toolkit'] | kaldi_decoding_scripts/utils/nnet/make_lstm_proto.py data_io.py core.py myDNN.py kaldi_decoding_scripts/utils/nnet/gen_hamm_mat.py kaldi_decoding_scripts/utils/nnet/make_nnet_proto.py kaldi_decoding_scripts/utils/filt.py vae.py kaldi_decoding_scripts/utils/nnet/make_blstm_proto.py save_raw_fea.py utils.py kaldi_decodin... | # The PyTorch-Kaldi Speech Recognition Toolkit <img src="pytorch-kaldi_logo.png" width="220" img align="left"> PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition systems. The DNN part is managed by PyTorch, while feature extraction, label computation, and decoding are ... | 134 |
Bala93/Context-aware-segmentation | ['medical image segmentation', 'semantic segmentation'] | ['A context based deep learning approach for unbalanced medical image segmentation'] | UNet-based-segmentation/prostate/losses.py GAN-based-segmentation/prostate/img_mask_transform.py dataset_creation/prepare_dataset_prostrate_dynamic_gan.py UNet-based-segmentation/prostate/utils.py GAN-based-segmentation/cardiac/train_global.py UNet-based-segmentation/prostate/dataset.py UNet-based-segmentation/cardiac/... | # Context-aware-segmentation > [A context based deep learning approach for unbalanced medical image segmentation](https://arxiv.org/abs/2001.02387) | 135 |
Bala93/Recon-GLGAN | ['mri reconstruction'] | ['Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction'] | models/gan/train.py common/utils.py data/mri_data.py models/gan/run.py common/evaluate.py models/recon_glgan/train.py models/recon_glgan/model.py models/recon_glgan/run.py models/gan/model.py evaluate psnr mse nmse Metrics ssim save_reconstructions SliceData SliceDataDev UNetUpBlock UNetConvBlock Discriminator UNet loa... | ## [Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction(Accepted at Machine Learning in Medical Image Reconstruction(MLMI), MICCAI Workshop)](https://arxiv.org/abs/1908.09262) ## ReconGLGAN illustration:  ## ReconGLGAN architecture:  Place the pyflann library in your current directory to replace the pyflann folder ### Cifar1... | 137 |
BaoWangMath/DP-LSSGD | ['stochastic optimization'] | ['DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM'] | RDP-Accountant/svrg_dp.py Logistic-Regression/Logistic_NonconstantLR_Eps10_Sigma0.py Logistic-Regression/Logistic_NonconstantLR_Eps10_Sigma1.py Logistic-Regression/LS_SGD.py RDP-Accountant/lssgd_dp.py RDP-Accountant/model_mnist.py RDP-Accountant/train_dpsgd_mnist.py RDP-Accountant/train_dpsvrg_mnist.py RDP-Accountant/s... | # DP-LSSGD | 138 |
BaoWangMath/EnResNet | ['adversarial defense', 'adversarial attack'] | ['ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies'] | ResNet20/main_pgd_enresnet5_20.py WideResNet34-10/main_pgd_wideresnet34_10_Validation.py ResNet20/resnet_cifar.py ResNet20/Attack_PGD_EnResNet_5_20.py WideResNet34-10/utils.py ResNet20/utils.py WideResNet34-10/resnet_cifar.py WideResNet34-10/Attack_PGD_WideResNet.py en_preactresnet20_cifar PreActBasicBlock en_preactres... | # EnResNet This repository consists PyTorch code for the paper: Bao Wang, Binjie Yuan, Zuoqiang Shi, Stanley J. Osher. EnResNet: ResNet Ensemble via the Feynman-Kac Formalism, arXiv:1811.10745, 2018 (https://arxiv.org/abs/1811.10745) The repo contains two subfolders for PGD adversarially training of ensemble of ResNet2... | 139 |
BaoWangMath/Graph-Structured-Recurrent-Neural-Nets- | ['adversarial defense', 'adversarial attack'] | ['ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies'] | src/srnn_tf.py src/FeatureGeneration.py src/Flu_Prediction.py ConvertSeriesToMatrix generate_data partition_to_three_parts partition_to_one_node_per_state load_graph_and_normalize DataReaderUSFlu edge_cell four_layer_lstm RNNPrediction edge_cell_2 node_cell img_enlarge SRNNBase _default_edge_cell TrainValSplit SRNNUndi... | BaoWangMath/Graph-Structured-Recurrent-Neural-Nets- | 140 |
BardOfCodes/Seg-Unravel | ['semantic segmentation'] | ['Per-Pixel Feedback for improving Semantic Segmentation'] | utils.py seg_fix.py demo.py global_variables.py main set_caffe_path seg_fix embed_fixations_gif get_blob embed_fixations get_heatmap imwrite image ArgumentParser get_top_fixations get_heatmap open list parse_args TEST seg_fix dump get_blob close Net embed_fixations set_caffe_path unique embed_fixations_gif join remove ... | BardOfCodes/Seg-Unravel | 141 |
BardOfCodes/pytorch_deeplab_large_fov | ['semantic segmentation'] | ['Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs'] | deeplab_large_fov.py test.py converter.py train_v1.py utils.py train_v2.py Net blur get_data_from_chunk_v2 get_parameters resize_label_batch crop get_test_data_from_chunk_v2 rotate adjust_learning_rate read_file chunker flip modules isinstance Conv2d Variable transpose from_numpy UpsamplingBilinear2d interp zeros int u... | BardOfCodes/pytorch_deeplab_large_fov | 142 |
Bartzi/kiss | ['scene text recognition'] | ['KISS: Keeping It Simple for Scene Text Recognition'] | common/datasets/text_recognition_image_dataset.py common/utils.py train_utils/create_video.py train_utils/disable_chain.py datasets/text_recognition/combine_npz_datasets.py optimizers/radam.py datasets/text_recognition/filter_word_length.py common/datasets/text_recognition_eval_dataset.py text/lstm_text_localizer.py tr... | # KISS Code for the paper [KISS: Keeping it Simple for Scene Text Recognition](https://arxiv.org/abs/1911.08400). This repository contains the code you can use in order to train a model based on our paper. You will also find instructions on how to access our model and also how to evaluate the model. # Pretrained Model ... | 143 |
Bartzi/see | ['scene text detection', 'scene text recognition'] | ['SEE: Towards Semi-Supervised End-to-End Scene Text Recognition'] | utils/create_video.py chainer/insights/svhn_bbox_plotter.py chainer/functions/disable_shearing.py chainer/train_svhn.py datasets/fsns/transform_gt.py chainer/insights/lstm_per_step_plotter.py datasets/svhn/filter_large_images.py chainer/train_mnist.py chainer/metrics/svhn_ctc_metrics.py chainer/datasets/concatenated_da... | # SEE: Towards Semi-Supervised End-to-End Scene Text Recognition Code for the AAAI 2018 publication "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition". You can read a preprint on [Arxiv](http://arxiv.org/abs/1712.05404) # Installation You can install the project directly on your PC or use a Docker contain... | 144 |
Bartzi/stn-ocr | ['optical character recognition', 'scene text detection', 'scene text recognition'] | ['STN-OCR: A single Neural Network for Text Detection and Text Recognition'] | mxnet/utils/extract_last_image_from_gif.py datasets/fsns/transform_gt.py mxnet/initializers/spn_initializer.py mxnet/metrics/ctc_metrics.py datasets/svhn/filter_large_images.py mxnet/utils/create_video.py mxnet/networks/text_rec.py mxnet/train_fsns.py mxnet/utils/datatypes.py datasets/fsns/slice_fsns_dataset.py mxnet/o... | # STN-OCR: A single Neural Network for Text Detection and Text Recognition This repository contains the code for the paper: [STN-OCR: A single Neural Network for Text Detection and Text Recognition](https://arxiv.org/abs/1707.08831) # Please note that we refined our approach and released new source code. You can find t... | 145 |
BattashB/Adaptive-and-Iteratively-Improving-Recurrent-Lateral-Connections | ['action recognition'] | ['Adaptive and Iteratively Improving Recurrent Lateral Connections'] | resnet.py trainer20.py resnetdy20.py resnet110 resnet20 ResNet LambdaLayer resnet44 test resnet1202 resnet56 resnet32 _weights_init BasicBlock weight kaiming_normal_ __name__ print | # Adaptive-and-Iteratively-Improving-Recurrent-Lateral-Connections An official Pytorch implementation of "Adaptive and Iteratively Improving Recurrent Lateral Connections" https://arxiv.org/abs/1910.11105 <p align="center"> <img src="BasicFeedback.png" alt="smiley" height="350px" width="600px"/> </p> ## Prerequisites... | 146 |
BeautyOfWeb/AffinityNet | ['few shot learning', 'type prediction', 'graph attention'] | ['AffinityNet: semi-supervised few-shot learning for disease type prediction'] | models/dense_factor_conv.py utils/sampler.py utils/utils.py affinitynet/test_graph_attention.py models/densenet.py models/factor_graph.py utils/gen_conv_params.py affinitynet/graph_attention.py utils/solver.py models/transformer.py WeightedFeature get_iterator GraphAttentionLayer GraphAttentionGroup DenseLinear get_par... | # AffinityNet AffinityNet with stacked kNN attention pooling layers for few-shot semi-supervised learning This repository is associated with the paper: AffinityNet: semi-supervised few-shot learning for disease type prediction For any questions about the code, please contact tianlema@buffalo.edu | 147 |
BenevolentAI/RELVM | ['relation extraction'] | ['Learning Informative Representations of Biomedical Relations with Latent Variable Models'] | run/unsup/__init__.py models/unsup/recognition/__init__.py models/sup/__init__.py tests/test_classification_pair.py models/unsup/generative/__init__.py exp_unsup.py tests/test_unsup.py trainers/sup/__init__.py run/__init__.py models/__init__.py trainers/__init__.py tests/test_classification_mention.py exp_classificatio... | # RELVM This repository contains the code accompanying the paper *"Learning Informative Representations of Biomedical Relations with Latent Variable Models", Harshil Shah and Julien Fauqueur, EMNLP SustaiNLP 2020*, (https://arxiv.org/abs/2011.10285). ## Requirements - Python 3.7 - Numpy >= 1.17.2 - Tensorflow >= 2.... | 148 |
BengaliAI/graphemePrepare | ['optical character recognition', 'multi label classification'] | ['A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes'] | data/extracted/pack.py data/extracted/purge.py data/scanned/transcribeGui.py data/packed/labelXGui.py labelXGui rightKey renameSave leftKey get configure set PhotoImage crop renameSave len get configure set PhotoImage crop renameSave | # Bengali.AI Computer Vision Challenge: Handwritten Bengali Grapheme Classification This repo contains code to extend/replicate the dataset present in the Kaggle [Bengali.AI Handwritten Grapheme Classification](www.kaggle.com/c/bengaliai-cv19). For the dataset, codes, discussions and leaderboards, visit the Kaggle comp... | 149 |
BerkeleyAutomation/dvrk-vismpc | ['video prediction'] | ['VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation'] | run.py call_network/process_dvrk_camera_image.py analysis_folding.py call_network/load_config.py call_network/load_net.py analysis.py tests/test_03_checkerboard_pick_test.py dvrkClothSim.py ZividCapture.py config.py dvrkArm.py tests/test_01_positions.py utils.py analysis_plots.py analyze_single analyze_group analyze_ic... | # Da Vinci Research Kit (dVRK) Code for Fabrics and Visual MPC *Update May 2020*: this is the code we used for the physical fabrics experiments with the dVRK. The master branch has the code for our RSS 2020 paper "VisuoSpatial Foresight (VSF) for Multi-Step, Multi-Task Fabric Manipulation": ``` @inproceedings{fabric_vs... | 150 |
BestActionNow/SemiSupBLI | ['bilingual lexicon induction'] | ['Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction'] | model/CSSBli.py model/PSSBli.py sinkhorn/sinkhorn_loss.py model/baseBli.py IO/__init__.py evaluation/eval.py sinkhorn/__init__.py model/__init__.py utils.py main.py evaluation/__init__.py IO/data.py load_batcher adaptLanguage make_directory to_cuda print_metrics read_from_yaml setup_logger setup_output_dir to_numpy dis... | # Semi-Supervised Bilingual Lexicon Induction with Two-Way Message Passing Mechanisms In this repository, We present the implementation of our two poposed semi-supervised approches **CSS** and **PSS** for BLI. ## Dependencies * python 3.7 * Pytorch * Numpy * Faiss ## How to get the datasets You need to download the *... | 151 |
BestSonny/SSTD | ['scene text detection'] | ['Single Shot Text Detector with Regional Attention'] | python/caffe/io.py python/caffe/test/test_python_layer.py python/caffe/net_spec.py examples/text/setup.py python/caffe/coord_map.py python/caffe/test/test_net.py tools/extra/resize_and_crop_images.py python/draw_net.py python/caffe/test/test_net_spec.py src/caffe/test/test_data/generate_sample_data.py python/caffe/draw... | [](LICENSE) # Single Shot Text Detector with Regional Attention ## Introduction **SSTD** is initially described in our [ICCV 2017 spotlight paper](https://arxiv.org/abs/1709.00138). [A third-party implementation of SSTD + Focal Loss](https://github.com/Hotaek... | 152 |
BigRedT/no_frills_hoi_det | ['human object interaction detection'] | ['No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques'] | utils/io.py exp/hoi_classifier/data/box_features.py data/hico/split_ids.py exp/hoi_classifier/data/assign_pose_to_human_candidates.py exp/hico_eval/sample_complexity_analysis.py data/hico/hoi_cls_count.py exp/hoi_classifier/vis/top_boxes_per_hoi.py exp/hoi_classifier/vis/faster_rcnn_aps.py exp/hoi_classifier/data/hoi_c... | # No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques By [Tanmay Gupta](http://tanmaygupta.info), [Alexander Schwing](http://alexander-schwing.de), and [Derek Hoiem](http://dhoiem.cs.illinois.edu) <p align="center"> <img src="imgs/teaser_wide.png"> </p> # Content -... | 153 |
BigRedT/vico | ['word embeddings'] | ['ViCo: Word Embeddings from Visual Co-occurrences'] | exp/glove/save_ae_embeddings.py exp/multi_sense_cooccur/vis/categories.py exp/multi_sense_cooccur/models/embeddings.py exp/multi_sense_cooccur/explore_merged_cooccur.py data/semeval_2018_10/compute_overlap_with_visual_words.py exp/cifar100/run.py data/semeval_2018_10/constants.py exp/glove/save_ae_visual_features.py ex... | # ViCo: Word Embeddings from Visual Co-occurrences By [Tanmay Gupta](http://tanmaygupta.info), [Alexander Schwing](http://alexander-schwing.de), and [Derek Hoiem](http://dhoiem.cs.illinois.edu) <p align="center"> <img src="imgs/teaser.png"> </p> # Contents - [Overview](#overview) - [Just give me pretrained ViCo](#j... | 154 |
BingCS/AtLoc | ['camera localization'] | ['AtLoc: Attention Guided Camera Localization'] | train.py network/atloc.py data/process_robotcar.py data/dataset_mean.py eval.py data/dataloaders.py tools/saliency_map.py tools/utils.py tools/options.py network/att.py RobotCar SevenScenes MF AtLoc FourDirectionalLSTM AtLocPlus AttentionBlock Options AverageMeter qlog AtLocCriterion mkdirs qexp Logger mkdir calc_vos_s... | [](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)  # [AtLoc: Attention Guided Camera Localization](https://arxiv.org/abs/1909.03557) - AAAI 2020 (Oral). [Bing Wang... | 155 |
BingyaoHuang/Neural-STE | ['image dehazing', 'denoising'] | ['Modeling Deep Learning Based Privacy Attacks on Physical Mail'] | src/python/pytorch_ssim/__init__.py src/python/Models.py src/python/utils.py src/python/train_Neural-STE.py src/python/trainNetwork.py WarpingNet Interpolate DehazingRefineNet NeuralSTE appendDataPoint trainNeuralSTE loadData evalNeuralSTE plotMontageMultirow computeLoss SimpleDataset fs optionToString make_grid_transp... | [AAAI'21] Modeling Deep Learning Based Privacy Attacks on Physical Mail <br><br> === <p align="center"> <img src='doc/net.png'> </p> ## Introduction PyTorch implementation of [Neural-STE (Neural-See-Through-Envelope)][1]. Please refer to [supplementary material (~68M)][2] for more results. ---- | 156 |
BjornarVass/Recsys | ['session based recommendations'] | ['Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions'] | tester_dynamic.py tester.py modules.py logger.py preprocess_trimmed.py datahandler_temporal.py dynamic_model.py preprocess_general.py hawkes.py hawkes_baseline.py hawkes_datahandler.py datahandler.py intra.py model.py PlainRNNDataHandler RNNDataHandler MHP DataHandler train_on_batch process_batch predict_on_batch maske... | # Hieararchical RNN recommender with temporal modeling The code for my master's thesis # Requirements Python 3 PyTorch, with CUDA support Numpy Scipy # Data ## Datasets LastFM:http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html | 157 |
Bkmz21/CompactCNNCascade | ['face detection'] | ['Compact Convolutional Neural Network Cascade for Face Detection'] | cntk/prepare_data.py | ## Compact Convolutional Neural Network Cascade ## This is a binary library for very fast detection of simple objects in images using CPU or GPU.<br> Implemented of the algorithm described in the following paper: I.A. Kalinovskiy, V.G. Spitsyn, Compact Convolutional Neural Network Cascade for Face Detection, http:/... | 158 |
BlackHC/BatchBALD | ['active learning'] | ['BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning'] | laaos_results/paper/cinic10_nispc20_pretrained_multibald_bald_k50_b10_885898.py laaos_results/paper/repeated_mnist_w_noise5_multibald_bald_k10_b10_531246.py src/acquisition_batch.py laaos_results/paper/cinic10_nispc20_pretrained_independent_bald_k50_b10_953083.py src/joint_entropy/unoptimized/test_exact_joint_probs.py ... | # BatchBALD **Note:** A more modular re-implementation can be found at https://github.com/BlackHC/batchbald_redux. --- This is the code drop for our paper [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://arxiv.org/abs/1906.08158). The code comes as is. See https://github.c... | 159 |
BlackHC/batchbald_redux | ['active learning'] | ['BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning'] | batchbald_redux/joint_entropy.py batchbald_redux/__init__.py batchbald_redux/_nbdev.py batchbald_redux/consistent_mc_dropout.py batchbald_redux/repeated_mnist.py setup.py batchbald_redux/active_learning.py batchbald_redux/batchbald.py get_subset_base_indices get_base_indices ActiveLearningData get_balanced_sample_indic... | # BatchBALD Redux > Clean reimplementation of \"BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning\" For an introduction & more information, see https://blackhc.github.io/BatchBALD/. The paper can be found at http://arxiv.org/abs/1906.08158. The documentation for this version can be fo... | 160 |
BogiHsu/Tacotron2-PyTorch | ['speech synthesis'] | ['Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions'] | train.py model/layers.py text/numbers.py model/model.py utils/logger.py mkgta.py text/symbols.py text/__init__.py utils/dataset.py utils/util.py utils/audio.py inference.py hparams.py text/cleaners.py text/cmudict.py utils/plot.py hparams load_model plot audio infer save_mel plot_data infer load_model save_mel files_to... | # Tacotron2-PyTorch Yet another PyTorch implementation of [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf). The project is highly based on [these](#References). I made some modification to improve speed and performance of both training and inference. #... | 161 |
Borda/pyImSegm | ['superpixels', 'semantic segmentation'] | ['Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut.', 'Detection and Localization of Drosophila Egg Chambers in Microscopy Images.', 'Region growing using superpixels with learned shape prior.'] | experiments_ovary_detect/run_ellipse_annot_match.py tests/test_pipelines.py experiments_ovary_detect/run_cut_segmented_objects.py tests/test_descriptors.py tests/test_region-growing.py experiments_ovary_centres/run_create_annotation.py experiments_segmentation/run_compute_stat_annot_segm.py imsegm/annotation.py imsegm/... | # Image segmentation toolbox [](https://github.com/Borda/pyImSegm/actions?query=workflow%3A%22CI+testing%22) [](https:/... | 162 |
BorealisAI/cross_domain_coherence | ['domain generalization'] | ['A Cross-Domain Transferable Neural Coherence Model'] | utils/lm_utils.py models/language_models.py run_lm_coherence.py utils/logging_utils.py eval.py utils/np_utils.py models/gan_models.py models/infersent_models.py prepare_data.py preprocess.py train_lm.py config.py models/coherence_models.py add_args.py run_bigram_coherence.py utils/data_utils.py add_bigram_args experime... | # Cross-Domain Coherence Modeling A Cross-Domain Transferable Neural Coherence Model Paper published in ACL 2019: [arxiv.org/abs/1905.11912](https://arxiv.org/abs/1905.11912) This implementation is based on PyTorch 0.4.1. ### Dataset To download the dataset: ``` python prepare_data.py ``` which includes WikiCoherence ... | 163 |
BorjaBalle/analytic-gaussian-mechanism | ['denoising'] | ['Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising'] | agm-example.py calibrateAnalyticGaussianMechanism caseA doubling_trick function_s_to_alpha sqrt binary_search | # Analytic Gaussian Mechanism Example Python implementation of the analytic Gaussian mechanism proposed in the [paper](https://arxiv.org/abs/1805.06530): > B. Balle and Y.-X. Wang. Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. International Conference on Machin... | 164 |
BreastGAN/experiment1 | ['adversarial attack'] | ['Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks'] | resources/model_utils.py models/base.py docker/run_config.py resources/data/mnist.py resources/data/loader.py resources/yapf_nbformat/yapf_nbformat.py resources/data/utils.py resources/image_utils.py resources/synthetic_data.py flags/flags_parser.py models/breast_cycle_gan_graph.py docker/jupyter_notebook_config.py mai... | BreastGAN/experiment1 | 165 |
BreastGAN/experiment2 | ['adversarial attack'] | ['Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks'] | resources/data/loader.py models/breast_cycle_gan/custom/conv/spectral_norm.py models/breast_cycle_gan/custom/gan.py models/utils/icnr.py notebooks/inference_tfrecord_to_png.py models/breast_cycle_gan/inference.py docker/jupyter_notebook_config.py resources/yapf_nbformat/yapf_nbformat.py models/breast_cycle_gan/custom/c... | BreastGAN/experiment2 | 166 |
Brendan-Reid1991/CFD-Algorithms | ['combinatorial optimization', 'experimental design'] | ['Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis'] | DummyFiles/SA/PositionDefinitions.py Multiplier_Dim3/SA/PropagateCircuit.py Multiplier_Dim3/PT/PositionDefinitions.py DummyFiles/PT/PT_DataCollection.py DummyFiles/SA/SA_DataCollection.py DummyFiles/PT/ParallelTempering_DebuggingFile.py DummyFiles/PT/PropagateCircuit.py DummyFiles/SA/SimulatedAnnealing_DebuggingFile.py... | # CFD-Algorithms Algorithms for solving circuit-fault-diagnosis problems These files aim to solve a circuit fault diagnosis (CFD) problem via annealing techniques. CFD problems are conceptually simple: given a circuit C, and some inputs X,Y and an output Z, if Z != C(X, Y) then a gate within the circuit must be faulty... | 167 |
BrixIA/Brixia-score-COVID-19 | ['data augmentation'] | ['Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation'] | src/BSNet/backbones/classification_models/classification_models/resnet/models.py src/BSNet/backbones/inception_v3.py src/BSNet/backbones/__init__.py src/BSNet/backbones/classification_models/classification_models/resnext/preprocessing.py src/BSNet/backbones/classification_models/classification_models/resnet/params.py s... | # BrixIA COVID-19 Project ## What do you find here Info, code (BS-Net), link to data (BrixIA COVID-19 Dataset annotated with Brixia-score), and additional material related to the [BrixIA COVID-19 Project](https://brixia.github.io/) ## Defs BrixIA COVID-19 Project: [go to the webpage](https://brixia.github.io/) Brixia s... | 168 |
BruceBinBoxing/Deep_Learning_Weather_Forecasting | ['weather forecasting'] | ['Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting'] | src/models/competition_model_class.py src/data/make_TrainAndVal_Data_from_nc.py src/weather_forecasting2018_eval/ensemble_2018102803_2/ensemble.py src/run/Load_model_and_predict.py src/models/weather_model.py src/data/utils.py src/models/seq2seq_class.py src/weather_forecasting2018_eval/ensemble_2018101503/ensemble.py ... | Deep Uncertainty Quantification (DUQ) ============================== DUQ: A Machine Learning Approach for Weather Forecasting > 1. Sequential deep uncertainty quantification (DUQ) produces more accurate weather forecasting based on the observation and NWP prediction. Our online rank-2 (CCIT007) in *Global AI Challenger... | 169 |
BruceBinBoxing/WF | ['weather forecasting'] | ['Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting'] | src/models/competition_model_class.py src/data/make_TrainAndVal_Data_from_nc.py src/weather_forecasting2018_eval/ensemble_2018102803_2/ensemble.py src/run/Load_model_and_predict.py src/models/weather_model.py src/data/utils.py src/models/seq2seq_class.py src/weather_forecasting2018_eval/ensemble_2018101503/ensemble.py ... | Deep Uncertainty Quantification (DUQ) ============================== DUQ: A Machine Learning Approach for Weather Forecasting > 1. Sequential deep uncertainty quantification (DUQ) produces more accurate weather forecasting based on the observation and NWP prediction. Our online rank-2 (CCIT007) in *Global AI Challenger... | 170 |
BruceBinBoxing/Weather_Forecasting | ['weather forecasting'] | ['Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting'] | src/models/competition_model_class.py src/data/make_TrainAndVal_Data_from_nc.py src/weather_forecasting2018_eval/ensemble_2018102803_2/ensemble.py src/run/Load_model_and_predict.py src/models/weather_model.py src/data/utils.py src/models/seq2seq_class.py src/weather_forecasting2018_eval/ensemble_2018101503/ensemble.py ... | Deep Uncertainty Quantification (DUQ) ============================== DUQ: A Machine Learning Approach for Weather Forecasting > 1. Sequential deep uncertainty quantification (DUQ) produces more accurate weather forecasting based on the observation and NWP prediction. Our online rank-2 (CCIT007) in *Global AI Challenger... | 171 |
BruceChanJianLe/Image-Text-Recognition | ['optical character recognition', 'scene text detection', 'curved text detection'] | ['EAST: An Efficient and Accurate Scene Text Detector'] | Phase 2/Text Recognition Algorithm.py Phase 1/Text detection Algorithm.py decode decode cos sin append float range | # README ## Description of algorithm and how to use it ### Phase 1 During Phase 1, I am developing an algorithm to detect the text existing in the images. The detected region will be used for Phase 2 which is text recognition. I am using 'EAST' as the text detector, which in full name it is called ' Efficient and Accur... | 172 |
CIA-Oceanix/GeoTrackNet | ['anomaly detection'] | ['GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection'] | runners.py bounds.py nested_utils.py data/calculate_AIS_mean.py data/csv2pkl.py models/vrnn.py distribution_utils.py contrario_utils.py geotracknet.py data/dataset_preprocessing.py data/datasets.py utils.py flags_config.py elbo always_resample_criterion never_resample_criterion fivo ess_criterion contrario_detection nC... | # GeoTrackNet TensorFlow implementation of the model proposed in "A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams" (https://ieeexplore.ieee.org/abstract/document/8631498) and "GeoTrackNet—A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks... | 173 |
CLUEbenchmark/CLGE | ['text summarization'] | ['LCSTS: A Large Scale Chinese Short Text Summarization Dataset'] | tasks/autotitle_baseline.py AutoTitle Evaluator CrossEntropy data_generator load_data | CLUEbenchmark/CLGE | 174 |
CODAIT/deep-histopath | ['whole slide images'] | ['A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology'] | deephistopath/wsi/filter.py v2/mrcnn/config.py v2/nucleus/nucleus_mitosis.py predict_mitoses_smooth.py preprocess_mitoses.py deephistopath/wsi/tiles.py deephistopath/visualization.py dist/mitosis_dist.py train_mitoses.py v2/mrcnn/parallel_model.py v2/mrcnn/visualize.py dist/utils.py v2/mrcnn/model.py v2/nucleus/mitosis... | <!-- {% comment %} Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may... | 175 |
CODEJIN/RHRNet | ['speech enhancement'] | ['RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement'] | Datasets.py STFTLoss.py Logger.py Radam.py Train.py Trace.py Modules.py Noam_Scheduler.py Arg_Parser.py Audio.py Recursive_Parse Spectrogram_Generate Audio_Prep Preemphasis Mel_Generate Inference_Collater Collater Calc_RMS Inference_Dataset Dataset Logger RHRNet GRU Log_Cosh_Loss Modified_Noam_Scheduler Noam_Scheduler ... | # RHRNet * This repository is a RHRNet unofficial code. * I added STFT loss additionally. * The following is the paper I referred: * [Abdulbaqi, J., Gu, Y., & Marsic, I. (2019). RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement. arXiv preprint arXiv:1904.07294.](https://arxiv.org/abs/190... | 176 |
COINtoolbox/RESSPECT | ['active learning'] | ['Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients'] | resspect/__init__.py resspect/tests/test_example.py resspect/batch_functions.py resspect/lightcurves_utils.py resspect/time_domain_loop.py resspect/tests/test_fixtures.py resspect/testing.py resspect/scripts/fit_dataset.py resspect/scripts/__init__.py resspect/tests/test_fit_lightcurves.py resspect/bazin.py resspect/te... | [](http://cosmostatistics-initiative.org/resspect/) # <img align="right" src="docs/images/logo_small.png" width="350"> RESSPECT ## Recommendation System for Spectroscopic follow-up This repository holds the pipeline of the RESSPECT project, built as par... | 177 |
COMP6248-Reproducability-Challenge/REPRODUCIBILITY-REPORT-THE-LOTTERY-TICKET-HYPOTHESIS | ['network pruning'] | ['The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks'] | archs/cifar100/resnet.py archs/cifar100/vgg.py archs/mnist/AlexNet.py archs/cifar10/AlexNet.py archs/cifar10/LeNet5.py archs/cifar10/resnet.py archs/mnist/resnet.py archs/cifar100/AlexNet.py archs/cifar10/vgg.py utils.py archs/mnist/vgg.py combine_plots.py archs/cifar100/fc1.py archs/cifar10/fc1.py archs/cifar10/densen... | # REPRODUCIBILITY-REPORT-THE-LOTTERY-TICKET-HYPOTHESIS This one is reproduction of 'THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS' paper \ The paper: https://arxiv.org/abs/1803.03635 \ The source code: https://github.com/rahulvigneswaran/Lottery-Ticket-Hypothesis-in-Pytorch \ Group name: SQL ... | 178 |
CQFIO/FastImageProcessing | ['style transfer'] | ['Fast Image Processing with Fully-Convolutional Networks'] | Single_Network/combined.py CAN24_AN/demo.py Parameterized_Network/parameterized.py lrelu identity_initializer nm prepare_data build lrelu identity_initializer nm prepare_data build lrelu identity_initializer nm one_hot_map prepare_data build Variable conv2d append range zeros | CQFIO/FastImageProcessing | 179 |
CR-Gjx/LeakGAN | ['text generation'] | ['Long Text Generation via Adversarial Training with Leaked Information'] | Synthetic Data/LeakGANModel.py Image COCO/convert.py Synthetic Data/Main.py Image COCO/LeakGANModel.py Image COCO/Main.py No Temperature/Synthetic Data/Main.py No Temperature/Synthetic Data/Discriminator.py Synthetic Data/target_lstm.py No Temperature/Synthetic Data/dataloader.py No Temperature/Image COCO/eval_bleu.py ... | # LeakGAN The code of research paper [Long Text Generation via Adversarial Training with Leaked Information](https://arxiv.org/abs/1709.08624). This paper has been accepted at the Thirty-Second AAAI Conference on Artificial Intelligence ([AAAI-18](https://aaai.org/Conferences/AAAI-18/)). ## Requirements * **Tensorflo... | 180 |
CRIPAC-DIG/A-PGNN | ['session based recommendations', 'machine translation'] | ['Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation'] | model_last.py transformer.py train_last.py record.py normalize decoder feedforward multihead_attention pos_encoding encoder to_float exp concat log expand_dims float range | # A-PGNN The code and dataset for our TKDE paper: Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation (https://ieeexplore.ieee.org/abstract/document/9226110). We have implemented our methods in Tensorflow. Here are two datasets we used in our paper. * Xing http://2016.recsyschal... | 181 |
CRIPAC-DIG/SR-GNN | ['session based recommendations'] | ['Session-based Recommendation with Graph Neural Networks'] | tensorflow_code/model.py tensorflow_code/main.py tensorflow_code/utils.py datasets/preprocess.py pytorch_code/utils.py pytorch_code/model.py pytorch_code/main.py process_seqs obtian_tra obtian_tes main GNN trans_to_cuda train_test trans_to_cpu SessionGraph forward data_masks build_graph split_validation Data GGNN Model... | # SR-GNN ## Paper data and code This is the code for the AAAI 2019 Paper: [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855). We have implemented our methods in both **Tensorflow** and **Pytorch**. Here are two datasets we used in our paper. After downloaded the datasets, you ca... | 182 |
CRIPAC-DIG/TAGNN | ['session based recommendations'] | ['TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation'] | utils.py main.py model.py main GNN trans_to_cuda train_test trans_to_cpu SessionGraph forward data_masks build_graph split_validation Data load validation Data trans_to_cuda time epoch print train_test valid_portion SessionGraph split_validation dataset range open is_available is_available trans_to_cuda get_slice model... | # TAGNN Implementation for the paper entitled "TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation" | 183 |
CSAILVision/GazeCapture | ['gaze estimation'] | ['Eye Tracking for Everyone'] | pytorch/main.py pytorch/ITrackerData.py pytorch/ITrackerModel.py pytorch/prepareDataset.py SubtractMean loadMetadata ITrackerData ITrackerModel FaceImageModel FaceGridModel ItrackerImageModel validate load_checkpoint AverageMeter save_checkpoint adjust_learning_rate main train str2bool cropImage readJson preparePath ma... | # Eye Tracking for Everyone Code, Dataset and Models ## Introduction This is the README file for the official code, dataset and model release associated with the 2016 CVPR paper, "Eye Tracking for Everyone". The dataset release is broken up into three parts: * **Data** (image files and associated metadata) * **Models**... | 184 |
CSAILVision/sceneparsing | ['scene parsing', 'semantic segmentation'] | ['Semantic Understanding of Scenes through the ADE20K Dataset'] | evaluationCode/utils_eval.py trainingCode/caffe/ade_layers.py pixelAccuracy intersectionAndUnion AdeSegDataLayer asarray histogram sum asarray | # Development Kit for MIT Scene Parsing Benchmark [NEW!] Our PyTorch implementation is released in the following repository: https://github.com/hangzhaomit/semantic-segmentation-pytorch ## Introduction Table of contents: - Overview of scene parsing benchmark - Benchmark details 1. Image list and annotations 2.... | 185 |
CUVL/Neural-Manifold-Ordinary-Differential-Equations | ['density estimation'] | ['Neural Manifold Ordinary Differential Equations'] | flows/utils.py test_densities/density_sphere.py flows/manifold.py test_densities/density_hyp.py distributions/vmf.py flows/hyperbolic.py flows/mcnf.py distributions/wnormal.py main_density.py flows/sphere.py main compute_loss _kl_vmf_uniform VonMisesFisher IveFunction Ive ive_fraction_approx ive_fraction_approx2 Hypers... | # Neural Manifold Ordinary Differential Equations (ODEs) We provide the code for [Neural Manifold ODEs](https://arxiv.org/abs/2006.10254) in this repository. Summary: We introduce Neural Manifold Ordinary Differential Equations, a manifold generalization of Neural ODEs, and construct Manifold Continuous Normalizing Flo... | 186 |
CVBase-Bupt/EndtoEndCroppingSystem | ['image cropping'] | ['An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic Photos'] | demo.py models/config.py models/RoiPoolingConv.py models/model.py utils.py main get_shape runn normalization recover_from_normalization recover_from_normalization_with_order add_offset Config EndToEndModel RoiPoolingConv_Boundary RoiPoolingConv save saliency_box_color log aesthetics_box_color open crop_out_path get_sha... | # End-to-End Cropping System This is an offical implemenation for **An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic Photos**. Given a source image, our algorithm could take actions step by step to find almost the best cropping window on source image. ## Get Start Install the pyth... | 187 |
CVLAB-Unibo/Semantic-Mono-Depth | ['depth estimation', 'monocular depth estimation', 'semantic segmentation'] | ['Geometry meets semantics for semi-supervised monocular depth estimation'] | monodepth_dataloader.py average_gradients.py utils/evaluation_utils.py utils/evaluate_kitti.py monodepth_main.py utils/shuffler.py utils.py bilinear_sampler.py monodepth_model.py utils/visualize_semantic.py average_gradients bilinear_sampler_1d_h string_length_tf MonodepthDataloader count_text_lines test post_process_d... | # Semantic-Mono-Depth  This repository contains the source code of Semantic-Mono-Depth, proposed in the paper "Geometry meets semantics for semi-supervised monocular depth estimation", ACCV 2018. If you use this code in your projects, please cite our paper: ``` @inproceedings{ramir... | 188 |
CVRL/OpenSourceIrisPAD | ['iris recognition', 'semantic segmentation'] | ['Open Source Presentation Attack Detection Baseline for Iris Recognition'] | python/BSIF_C/setup.py python/manager.py python/filter.py extract s2i generateHistogram manager load int copyMakeBorder ones float64 filter2D int64 histogram BORDER_WRAP empty range str asarray File generateHistogram close imread pyrDown | # OpenSourceIrisPAD (v2 - 13 April 2019) This repo contains the open-source implementation of iris PAD based on BSIF and a fusion of multiple classifiers, and is based on Jay Doyle's paper: ["Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF", IEEE Access, 2015](https://ieeexplore.ieee.org/docu... | 189 |
CVRL/RaspberryPiOpenSourceIris | ['iris recognition', 'iris segmentation', 'semantic segmentation'] | ['Open Source Iris Recognition Hardware and Software with Presentation Attack Detection', 'Open Source Presentation Attack Detection Baseline for Iris Recognition', 'Iris Presentation Attack Detection Based on Photometric Stereo Features'] | utils/utils.py CCNet/modules/network.py CCNet/modules/transform.py PAD/OSPAD_3D.py CCNet/modules/criterion.py segmentation/SegNet.py OSIRIS_SEGM/OSIRIS_SEGM.py PAD/OSPAD_2D.py CCNet/modules/dataset.py PAD/BSIF_C/test.py utils/test_camera.py segmentation/UNet.py segmentation/custom_layers.py CCNet/torch2keras.py recogni... | # RaspberryPiOpenSourceIris Official Repo for IJCB 2020 Paper: Open Source Iris Recognition Hardware and Software with Presentation Attack Detection ([https://arxiv.org/abs/2008.08220](https://arxiv.org/abs/2008.08220))<br/> *Zhaoyuan Fang, Adam Czajka<br/>* <img src="Teaser.png" width="800" > ## Cite If you find this ... | 190 |
CVRL/iris-recognition-OTS-DNN | ['iris recognition', 'iris segmentation', 'semantic segmentation'] | ['Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks'] | code/augment.py | # iris-recognition-OTS-DNN Code and models for the ICB 2019 paper: Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks. Pre-print available at: https://arxiv.org/abs/1901.01028 ## Contents ### code You will need to edit the scripts and programs below to point to the correct p... | 191 |
CVxTz/fingerprint_denoising | ['denoising'] | ['Deep End-to-end Fingerprint Denoising and Inpainting'] | code/aug_utils.py code/baseline_aug.py code/baseline_aug_predict.py random_crop random_channel_shift random_zoom random_saturation shift random_rotate random_flip random_brightness random_gray random_shear random_augmentation random_contrast random_shift custom_activation get_unet read_gt read_input gen batch flip_axis... | CVxTz/fingerprint_denoising | 192 |
CW-Huang/NAF | ['density estimation', 'speech synthesis'] | ['Neural Autoregressive Flows'] | external_maf/power.py external_maf/util.py external_maf/cifar10.py external_maf/datasets/__init__.py download_datasets.py external_maf/__init__.py sf_sinewave.py external_maf/mnist.py external_maf/miniboone.py maf_experiments.py external_maf/bsds300.py steps_plot.py external_maf/hepmass.py vae_experiments.py ops.py ext... | # NAF Experiments for the Neural Autoregressive Flows paper This repo depends on another library for pytorch modules: https://github.com/CW-Huang/torchkit To download datasets, please modify L21-24 of `download_datasets.py`. | 193 |
CZHQuality/AAA-Pix2pix | ['adversarial attack'] | ['A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories'] | code/Main_Ensemble_Attack_2.py code/data/aligned_dataset_My_2.py code/Options.py code/Main_Single_Image_Space_Attack.py code/Main_Single_Feature_Space_Attack.py code/Main_Ensemble_Attack_IF.py code/data/aligned_dataset.py code/data/base_data_loader.py code/Loss_functions.py code/Attack_methods_library.py code/config_gl... | # SMGEA: Serial-Mini-Group-Ensemble-Attack **SMGEA** is a new **Black-Box** Adversarial Attack against various **Pixel-to-Pixel** Tasks, such as **Saliency Detection, Depth Estimation, Image Translation, etc.** This code repository is an Open-Source Toolbox based on Pytorch Platform. A **preliminary version** of this r... | 194 |
CZWin32768/seqmnist | ['machine translation'] | ['Sequence Level Training with Recurrent Neural Networks'] | seqmnist/main.py seqmnist/field.py seqmnist/trainer.py seqmnist/seqmnist_dataset.py setup.py seqmnist/img_encoder.py TgtField SrcField EncoderCNN2D conf build_dataset train build_model SeqMnistDataset SeqMnistExample SupervisedTrainer print parameters EncoderCNN2D uniform_ DecoderRNN Seq2seq train_path dev_path print S... | # Sequence MNIST ## Dataset https://drive.google.com/open?id=1I8NbuUc0vF3igpCihhryVuiNd31nlwkh ## Requirements For `seq2seq` package, refer to https://github.com/CZWin32768/pytorch-seq2seq. ``` imageio torchtext torch tqdm | 195 |
Caiyq2019/DNF | ['speaker recognition'] | ['Deep Normalization for Speaker Vectors'] | train.py tsne.py data_load.py utils.py score.py model.py data_load dataset_prepare load_data_normalised get_data get_mask FlowSequential MADE MaskedLinear compute_eer supervise_mean_var compute_idr data_load Cosine_score load_trails cosine_scoring_by_trails initial data_normlize supervise_mean_var train get_data main p... | # DNF A Pytorch implementations of DNF for author's article ["Deep normalization for speaker vectors"](https://arxiv.org/abs/2004.04095) The neural network structure is based on "Masked Autoregressive Flow", and the source code from [ikostrikov](https://github.com/ikostrikov/pytorch-flows/blob/master/README.md) ## Data... | 196 |
Caiyq2019/MG | ['speaker recognition'] | ['Deep Speaker Vector Normalization with Maximum Gaussianality Training'] | train.py tsne.py data_load.py utils.py score.py model.py data_load dataset_prepare load_data_normalised get_data get_mask FlowSequential MADE MaskedLinear compute_eer supervise_mean_var compute_idr data_load Cosine_score load_trails cosine_scoring_by_trails initial supervise_mean_var angle_Gaussian_log_likelihood L2_Ga... | # MG Training (Maximum Gaussianality Training) A Pytorch implementations of MG training for author's article ["Deep Speaker Vector Normalization with Maximum Gaussianality Training"](https://arxiv.……) This is a general Gaussian distribution training method and can be used in any task that requires Gaussian distribution... | 197 |
CalayZhou/MBNet | ['pedestrian detection'] | ['Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems'] | keras_MBNet/model/model_AP_IAFA.py keras_MBNet/parallel_model.py keras_MBNet/utils/timer.py keras_MBNet/model/__init__.py keras_MBNet/model/deform_conv.py keras_MBNet/bbox_process.py keras_MBNet/model/base_model.py train.py demo_video.py keras_MBNet/nms/py_cpu_nms.py keras_MBNet/utils/__init__.py keras_MBNet/model/Fixe... | # MBNet Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems (ECCV 2020) - paper download: https://arxiv.org/pdf/2008.03043.pdf - the introduction PPT: https://github.com/CalayZhou/MBNet/blob/master/MBNet-3128.pdf - video demo: https://www.bilibili.com/video/BV1Hi4y137aS # Usage ## 1. ... | 198 |
Canjie-Luo/MORAN_v2 | ['scene text recognition'] | ['A Multi-Object Rectified Attention Network for Scene Text Recognition'] | tools/dataset.py demo.py models/moran.py models/morn.py test.py models/asrn_res.py inference.py tools/utils.py main.py models/fracPickup.py Recognizer val trainBatch ResNet BidirectionalLSTM ASRN AttentionCell Attention Residual_block fracPickup MORAN MORN lmdbDataset randomSequentialSampler resizeNormalize averager lo... | # MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition  | <center>Python 2.7</center> | <center>Python 3.6</center> | | :---: | :---: | | <center>[](https://tra... | 199 |
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