task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Active Speaker Detection > Fraud Detection | Yelp-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | Averaged Precision | 83.56 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | F1 Macro | 86.35 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | G-mean | 84.91 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | JA-GNN | https://arxiv.org/abs/2411.05857v2 | AUC-ROC | 95.11 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | GTAN | https://arxiv.org/abs/2412.18287v1 | AUC-ROC | 94.98 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | GTAN | https://arxiv.org/abs/2412.18287v1 | Averaged Precision | 82.41 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | BOLT-GRAPH | https://arxiv.org/abs/2303.17727v4 | AUC-ROC | 93.18 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | SplitGNN | https://dl.acm.org/doi/10.1145/3583780.3615067 | AUC-ROC | 92.03 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | GAT+JK | https://arxiv.org/abs/2104.01404v2 | AUC-ROC | 90.04 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | RLC-GNN | https://www.mdpi.com/2076-3417/11/12/5656/htm | AUC-ROC | 85.44 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | RioGNN | https://arxiv.org/abs/2104.07886v2 | AUC-ROC | 83.54 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | PC-GNN | https://dl.acm.org/doi/abs/10.1145/3442381.3449989 | AUC-ROC | 79.87 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | PC-GNN | https://dl.acm.org/doi/abs/10.1145/3442381.3449989 | Averaged Precision | 48.10 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | CARE-GNN | https://arxiv.org/abs/2008.08692v1 | AUC-ROC | 75.70 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | CARE-GNN | https://arxiv.org/abs/2008.08692v1 | Averaged Precision | 42.68 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GCN | https://arxiv.org/abs/2405.19383v3 | AUPRC | 0.5946 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GCN | https://arxiv.org/abs/2405.19383v3 | AUC | 0.8329 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GraphSAGE | https://arxiv.org/abs/2405.19383v3 | AUPRC | 0.6312 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GraphSAGE | https://arxiv.org/abs/2405.19383v3 | AUC | 0.8279 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GAT | https://arxiv.org/abs/2405.19383v3 | AUPRC | 0.5886 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GAT | https://arxiv.org/abs/2405.19383v3 | AUC | 0.8102 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GIN | https://arxiv.org/abs/2405.19383v3 | AUPRC | 0.5517 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | GIN | https://arxiv.org/abs/2405.19383v3 | AUC | 0.8089 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | Node2vec | https://arxiv.org/abs/2405.19383v3 | AUPRC | 0.0594 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | Node2vec | https://arxiv.org/abs/2405.19383v3 | AUC | 0.5263 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | Deepwalk | https://arxiv.org/abs/2405.19383v3 | AUPRC | 0.0488 |
Active Speaker Detection > Fraud Detection | Elliptic Dataset | Deepwalk | https://arxiv.org/abs/2405.19383v3 | AUC | 0.4500 |
Active Speaker Detection > Fraud Detection | BAF – Variant III | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 41.83% |
Active Speaker Detection > Fraud Detection | FDCompCN | SplitGNN | https://dl.acm.org/doi/10.1145/3583780.3615067 | AUC-ROC | 68.98 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | AUC-ROC | 97.91 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | Averaged Precision | 92.18 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | F1 Macro | 93.48 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | G-mean | 92.03 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | GTAN | https://arxiv.org/abs/2412.18287v1 | AUC-ROC | 97.50 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | GTAN | https://arxiv.org/abs/2412.18287v1 | Averaged Precision | 89.26 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | RLC-GNN | https://www.mdpi.com/2076-3417/11/12/5656/htm | AUC-ROC | 97.48 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | RioGNN | https://arxiv.org/abs/2104.07886v2 | AUC-ROC | 96.19 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | PC-GNN | https://dl.acm.org/doi/abs/10.1145/3442381.3449989 | AUC-ROC | 95.86 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | PC-GNN | https://dl.acm.org/doi/abs/10.1145/3442381.3449989 | Averaged Precision | 85.49 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | CARE-GNN | https://arxiv.org/abs/2008.08692v1 | AUC-ROC | 89.73 |
Active Speaker Detection > Fraud Detection | Amazon-Fraud | CARE-GNN | https://arxiv.org/abs/2008.08692v1 | Averaged Precision | 82.19 |
Object Rearrangement | Open6DOR V2 | SoFar | https://arxiv.org/abs/2502.13143v1 | pos-level0 | 96.0 |
Object Rearrangement | Open6DOR V2 | SoFar | https://arxiv.org/abs/2502.13143v1 | 6-DoF | 48.7 |
Object Rearrangement | Open6DOR V2 | SoFar | https://arxiv.org/abs/2502.13143v1 | pos-level1 | 81.5 |
Object Rearrangement | Open6DOR V2 | SoFar | https://arxiv.org/abs/2502.13143v1 | rot-level0 | 68.6 |
Object Rearrangement | Open6DOR V2 | SoFar | https://arxiv.org/abs/2502.13143v1 | rot-level1 | 42.2 |
Object Rearrangement | Open6DOR V2 | SoFar | https://arxiv.org/abs/2502.13143v1 | rot-level2 | 70.1 |
Object Rearrangement | Open6DOR V2 | Open6DOR | https://pku-epic.github.io/Open6DOR/ | pos-level0 | 60.3 |
Object Rearrangement | Open6DOR V2 | Open6DOR | https://pku-epic.github.io/Open6DOR/ | 6-DoF | 35.6 |
Object Rearrangement | Open6DOR V2 | Open6DOR | https://pku-epic.github.io/Open6DOR/ | pos-level1 | 78.6 |
Object Rearrangement | Open6DOR V2 | Open6DOR | https://pku-epic.github.io/Open6DOR/ | rot-level0 | 45.7 |
Object Rearrangement | Open6DOR V2 | Open6DOR | https://pku-epic.github.io/Open6DOR/ | rot-level1 | 32.5 |
Object Rearrangement | Open6DOR V2 | Open6DOR | https://pku-epic.github.io/Open6DOR/ | rot-level2 | 49.8 |
Object Rearrangement | Open6DOR V2 | Dream2Real | https://arxiv.org/abs/2312.04533v2 | pos-level0 | 11.0 |
Object Rearrangement | Open6DOR V2 | Dream2Real | https://arxiv.org/abs/2312.04533v2 | 6-DoF | 13.5 |
Object Rearrangement | Open6DOR V2 | Dream2Real | https://arxiv.org/abs/2312.04533v2 | pos-level1 | 17.2 |
Object Rearrangement | Open6DOR V2 | Dream2Real | https://arxiv.org/abs/2312.04533v2 | rot-level0 | 37.3 |
Object Rearrangement | Open6DOR V2 | Dream2Real | https://arxiv.org/abs/2312.04533v2 | rot-level1 | 27.6 |
Object Rearrangement | Open6DOR V2 | Dream2Real | https://arxiv.org/abs/2312.04533v2 | rot-level2 | 26.2 |
Object Rearrangement | Open6DOR V2 | GPT-4V | https://arxiv.org/abs/2303.08774v5 | pos-level0 | 39.1 |
Object Rearrangement | Open6DOR V2 | GPT-4V | https://arxiv.org/abs/2303.08774v5 | 6-DoF | - |
Object Rearrangement | Open6DOR V2 | GPT-4V | https://arxiv.org/abs/2303.08774v5 | pos-level1 | 46.8 |
Object Rearrangement | Open6DOR V2 | GPT-4V | https://arxiv.org/abs/2303.08774v5 | rot-level0 | 9.1 |
Object Rearrangement | Open6DOR V2 | GPT-4V | https://arxiv.org/abs/2303.08774v5 | rot-level1 | 6.9 |
Object Rearrangement | Open6DOR V2 | GPT-4V | https://arxiv.org/abs/2303.08774v5 | rot-level2 | 11.7 |
Object Rearrangement | Open6DOR V2 | VoxPoser | https://arxiv.org/abs/2307.05973v2 | pos-level0 | 21.7 |
Object Rearrangement | Open6DOR V2 | VoxPoser | https://arxiv.org/abs/2307.05973v2 | 6-DoF | - |
Object Rearrangement | Open6DOR V2 | VoxPoser | https://arxiv.org/abs/2307.05973v2 | pos-level1 | 35.6 |
Object Rearrangement | Open6DOR V2 | VoxPoser | https://arxiv.org/abs/2307.05973v2 | rot-level0 | - |
Object Rearrangement | Open6DOR V2 | VoxPoser | https://arxiv.org/abs/2307.05973v2 | rot-level1 | - |
Object Rearrangement | Open6DOR V2 | VoxPoser | https://arxiv.org/abs/2307.05973v2 | rot-level2 | - |
class-incremental learning | cifar100 | EWC | http://arxiv.org/abs/1612.00796v2 | 10-stage average accuracy | 50.53 |
ECG Digitization | ECG-Image-Database | ECG-Digitiser | https://arxiv.org/abs/2410.14185v1 | SNR | 12.15 |
ECG Digitization | ECG-Image-Database | BAPORLab | https://cinc.org/archives/2024/pdf/CinC2024-227.pdf | SNR | 5.493 |
ECG Digitization | ECG-Image-Database | WAVIE | https://cinc.org/archives/2024/pdf/CinC2024-229.pdf | SNR | 5.469 |
Multi-class Classification | Reuters-52 | SVM (tficf) | http://arxiv.org/abs/1012.2609v4 | Macro F1 | 73.9 |
Multi-class Classification | Training and validation dataset of capsule vision 2024 challenge. | Multi-Model Ensemble | https://arxiv.org/abs/2410.18879v2 | Mean AUC | 0.9908 |
Multi-class Classification | COVID-19 CXR Dataset | COVID-CXNet | https://arxiv.org/abs/2006.13807v2 | Accuracy (%) | 94.2 |
Multi-class Classification | COVID chest X-ray | COVID-ResNet | https://arxiv.org/abs/2003.14395v1 | F1 score | 0.8967 |
Multi-class Classification | TII-SSRC-23 | Extra Trees | https://arxiv.org/abs/2310.10661v1 | F1-Score | 93.36 |
Video Anomaly Detection | HR-ShanghaiTech | PoseWatch-H | https://arxiv.org/abs/2408.15185v1 | AUC | 87.23 |
Video Anomaly Detection | HR-ShanghaiTech | MoPRL | https://arxiv.org/abs/2112.03649v2 | AUC | 84.3 |
Video Anomaly Detection | HR-ShanghaiTech | TSGAD | https://arxiv.org/abs/2406.15395v1 | AUC | 81.77 |
Video Anomaly Detection | HR-ShanghaiTech | TrajREC | https://arxiv.org/abs/2311.01851v1 | AUC | 77.9 |
Video Anomaly Detection | HR-ShanghaiTech | MoCoDAD | https://arxiv.org/abs/2307.07205v3 | AUC | 77.6 |
Video Anomaly Detection | HR-ShanghaiTech | COSKAD-euclidean | https://arxiv.org/abs/2301.09489v5 | AUC | 77.1 |
Video Anomaly Detection | HR-ShanghaiTech | Multi-timescale Prediction | https://arxiv.org/abs/1908.04321v1 | AUC | 77.0 |
Video Anomaly Detection | HR-ShanghaiTech | COSKAD-hyperbolic | https://arxiv.org/abs/2301.09489v5 | AUC | 75.6 |
Video Anomaly Detection | HR-ShanghaiTech | MPED-RNN | http://arxiv.org/abs/1903.03295v2 | AUC | 75.4 |
Video Anomaly Detection | HR-ShanghaiTech | COSKAD-radial | https://arxiv.org/abs/2301.09489v5 | AUC | 75.2 |
Video Anomaly Detection | HR-ShanghaiTech | BiPOCO | https://arxiv.org/abs/2207.02281v1 | AUC | 74.9 |
Video Anomaly Detection | HR-ShanghaiTech | GEPC | https://arxiv.org/abs/1912.11850v2 | AUC | 74.8 |
Video Anomaly Detection | HR-ShanghaiTech | Pred | http://arxiv.org/abs/1712.09867v3 | AUC | 72.7 |
Video Anomaly Detection | HR-ShanghaiTech | Conv-AE | http://arxiv.org/abs/1604.04574v1 | AUC | 69.8 |
Video Anomaly Detection | Ped2 | HF2-VAD | https://arxiv.org/abs/2108.06852v1 | AUC | 0.993 |
Video Anomaly Detection | IITB Corridor | VideoPatchCore | https://arxiv.org/abs/2409.16225v5 | AUC | 76.4% |
Video Anomaly Detection | HR-Avenue | TrajREC | https://arxiv.org/abs/2311.01851v1 | AUC | 89.4 |
Video Anomaly Detection | HR-Avenue | MoCoDAD | https://arxiv.org/abs/2307.07205v3 | AUC | 89.0 |
Video Anomaly Detection | HR-Avenue | Multi-timescale Prediction | https://arxiv.org/abs/1908.04321v1 | AUC | 88.33 |
Video Anomaly Detection | HR-Avenue | COSKAD-euclidean | https://arxiv.org/abs/2301.09489v5 | AUC | 87.8 |
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