Long Tail Learning On Cifar 10 Lt R 10
평가 지표
Error Rate
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Error Rate |
|---|---|
| rebalanced-siamese-contrastive-mining-for | 8.0 |
| test-agnostic-long-tailed-recognition-by-test | 9.2 |
| global-and-local-mixture-consistency-1 | 5 |
| long-tailed-visual-recognition-via-gaussian-1 | 10.77 |
| exploring-balanced-feature-spaces-for | 12.00 |
| learning-imbalanced-data-with-vision | 8.7 |
| metasaug-meta-semantic-augmentation-for-long | 10.32 |
| rethinking-the-value-of-labels-for-improving | 11.47 |
| elf-an-early-exiting-framework-for-long | 12.00 |
| a-simple-episodic-linear-probe-improves | 11.3 |
| long-tailed-classification-by-keeping-the-1 | 11.5 |
| learning-imbalanced-data-with-vision | 10.1 |
| improving-calibration-for-long-tailed-1 | 10 |
| towards-calibrated-model-for-long-tailed | 12.20 |
| safa-sample-adaptive-feature-augmentation-for | 10.61 |
| learning-imbalanced-datasets-with-label | 13.61 |
| m2m-imbalanced-classification-via-major-to | 12.5 |
| towards-calibrated-model-for-long-tailed | 10.34 |
| a-unified-generalization-analysis-of-re | 8.18 |
| decoupling-representation-and-classifier-for | 8.9 |
| influence-balanced-loss-for-imbalanced-visual-1 | 12.93 |
| learning-imbalanced-data-with-vision | 10.5 |
| escaping-saddle-points-for-effective | 10.6 |
| test-agnostic-long-tailed-recognition-by-test | 10.3 |
| global-and-local-mixture-consistency-1 | 5.15 |
| improving-tail-class-representation-with | 10.3 |
| class-balanced-loss-based-on-effective-number | 12.90 |
| bbn-bilateral-branch-network-with-cumulative | 11.7 |
| learning-imbalanced-datasets-with-label | 13.21 |
| long-tailed-recognition-by-learning-from-1 | 8.8 |
| sure-survey-recipes-for-building-reliable-and | 5.04 |
| decoupling-representation-and-classifier-for | 9.0 |
| balanced-contrastive-learning-for-long-tailed-1 | 8.9 |
| reslt-residual-learning-for-long-tailed | 10.3 |
| class-balanced-loss-based-on-effective-number | 13.46 |
| long-tail-learning-with-attributes | 11.84 |
| delving-deep-into-simplicity-bias-for-long | 7.9 |
| learning-imbalanced-data-with-vision | 9.3 |
| balanced-meta-softmax-for-long-tailed-visual | 8.7 |
| imagine-by-reasoning-a-reasoning-based | 20.11 |
| parametric-contrastive-learning | 9.14 |
| learning-imbalanced-datasets-with-label | 11.84 |
| equalization-loss-for-long-tailed-object | 9.8 |
| leveraging-angular-information-between | 10.16 |
| targeted-supervised-contrastive-learning-for | 11.3 |
| learning-imbalanced-data-with-vision | 11.4 |
| curvature-balanced-feature-manifold-learning | 10.1 |
| contrastive-learning-based-hybrid-networks | 8.9 |
| disentangling-label-distribution-for-long | 11.22 |
| long-tailed-classification-by-keeping-the-1 | 12.63 |