Learning With Noisy Labels On Cifar 10N
المقاييس
Accuracy (mean)
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | Accuracy (mean) |
|---|---|
| robust-training-under-label-noise-by-over | 95.61 |
| imprecise-label-learning-a-unified-framework | 95.47 |
| learning-with-instance-dependent-label-noise-1 | 95.25 |
| النموذج 4 | 87.77 |
| clusterability-as-an-alternative-to-anchor | 91.97 |
| making-deep-neural-networks-robust-to-label | 88.24 |
| sample-prior-guided-robust-model-learning-to | 96.11 |
| co-teaching-robust-training-of-deep-neural | 91.20 |
| early-learning-regularization-prevents | 94.83 |
| generative-noisy-label-learning-by-implicit | 92.57 |
| when-optimizing-f-divergence-is-robust-with-1 | 91.64 |
| promix-combating-label-noise-via-maximizing | 97.39 |
| early-learning-regularization-prevents | 92.38 |
| making-deep-neural-networks-robust-to-label | 88.13 |
| peer-loss-functions-learning-from-noisy | 90.75 |
| does-label-smoothing-mitigate-label-noise | 91.57 |
| generalized-cross-entropy-loss-for-training | 87.85 |
| 190600189 | 88.52 |
| combating-noisy-labels-by-agreement-a-joint | 91.44 |
| learning-with-instance-dependent-label-noise-1 | 91.23 |
| understanding-and-improving-early-stopping | 94.66 |
| how-does-disagreement-help-generalization | 90.61 |
| dividemix-learning-with-noisy-labels-as-semi-1 | 95.01 |
| psscl-a-progressive-sample-selection | 96.41 |
| provably-end-to-end-label-noise-learning | 89.70 |
| understanding-generalized-label-smoothing | 91.97 |