Learning With Noisy Labels On Cifar 10N 3
평가 지표
Accuracy (mean)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy (mean) |
|---|---|
| generalized-cross-entropy-loss-for-training | 87.58 |
| when-optimizing-f-divergence-is-robust-with-1 | 89.55 |
| clusterability-as-an-alternative-to-anchor | 90.74 |
| making-deep-neural-networks-robust-to-label | 86.86 |
| learning-with-instance-dependent-label-noise-1 | 89.79 |
| combating-noisy-labels-by-agreement-a-joint | 90.11 |
| does-label-smoothing-mitigate-label-noise | 89.82 |
| early-learning-regularization-prevents | 91.41 |
| provably-end-to-end-label-noise-learning | 88.19 |
| robust-training-under-label-noise-by-over | 95.39 |
| co-teaching-robust-training-of-deep-neural | 90.15 |
| how-does-disagreement-help-generalization | 89.54 |
| making-deep-neural-networks-robust-to-label | 87.04 |
| psscl-a-progressive-sample-selection | 96.49 |
| generative-noisy-label-learning-by-implicit | 91.83 |
| learning-with-instance-dependent-label-noise-1 | 94.74 |
| imprecise-label-learning-a-unified-framework | 95.13 |
| 모델 18 | 85.16 |
| understanding-generalized-label-smoothing | 90.13 |
| peer-loss-functions-learning-from-noisy | 88.57 |
| dividemix-learning-with-noisy-labels-as-semi-1 | 89.97 |
| early-learning-regularization-prevents | 94.34 |
| 190600189 | 87.79 |