Image Classification On Clothing1M Using
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Accuracy | Paper Title | Repository |
|---|---|---|---|
| CleanNet w_soft | 79.90 | CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise | - |
| DMLP-DivideMix | 78.23% | Learning from Noisy Labels with Decoupled Meta Label Purifier | - |
| Forward | 80.27 | Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach | - |
| FasTEN | 77.83% | Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection | - |
| L2B (ResNet-18) | 77.5 ± 0.2% | L2B: Learning to Bootstrap Robust Models for Combating Label Noise | - |
| MLC | 75.78% | Meta Label Correction for Noisy Label Learning | - |
| CurriculumNet | 81.5% | CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images | - |
| EMLC (k=1) | 79.35% | Enhanced Meta Label Correction for Coping with Label Corruption | - |
| PUDistill | 77.70 | Training Classifiers that are Universally Robust to All Label Noise Levels | - |
0 of 9 row(s) selected.