Learning With Noisy Labels On Cifar 10N 2
Metrics
Accuracy (mean)
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy (mean) |
|---|---|
| generative-noisy-label-learning-by-implicit | 91.42 |
| co-teaching-robust-training-of-deep-neural | 90.30 |
| peer-loss-functions-learning-from-noisy | 88.76 |
| generalized-cross-entropy-loss-for-training | 87.70 |
| learning-with-instance-dependent-label-noise-1 | 94.88 |
| early-learning-regularization-prevents | 94.20 |
| making-deep-neural-networks-robust-to-label | 86.28 |
| 190600189 | 87.71 |
| combating-noisy-labels-by-agreement-a-joint | 90.21 |
| robust-training-under-label-noise-by-over | 95.31 |
| learning-with-instance-dependent-label-noise-1 | 89.91 |
| making-deep-neural-networks-robust-to-label | 86.14 |
| early-learning-regularization-prevents | 91.61 |
| psscl-a-progressive-sample-selection | 96.21 |
| dividemix-learning-with-noisy-labels-as-semi-1 | 90.90 |
| Model 16 | 86.46 |
| clusterability-as-an-alternative-to-anchor | 90.75 |
| does-label-smoothing-mitigate-label-noise | 89.35 |
| understanding-generalized-label-smoothing | 90.37 |
| imprecise-label-learning-a-unified-framework | 95.04 |
| when-optimizing-f-divergence-is-robust-with-1 | 89.79 |
| provably-end-to-end-label-noise-learning | 88.27 |
| how-does-disagreement-help-generalization | 89.47 |