Long Tail Learning On Cifar 10 Lt R 100
评估指标
Error Rate
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | Error Rate |
|---|---|
| escaping-saddle-points-for-effective | 17.6 |
| long-tail-learning-with-attributes | 20.37 |
| rethinking-the-value-of-labels-for-improving | 22.17 |
| long-tailed-visual-recognition-via-gaussian-1 | 17.32 |
| do-deep-networks-transfer-invariances-across-1 | 21.24 |
| enhanced-long-tailed-recognition-with | 13.93 |
| do-we-really-need-a-learnable-classifier-at | 23.5 |
| lpt-long-tailed-prompt-tuning-for-image | 10.9 |
| escaping-saddle-points-for-effective | 10.82 |
| feature-balanced-loss-for-long-tailed-visual-1 | 17.54 |
| ace-ally-complementary-experts-for-solving | 18.6 |
| deit-lt-distillation-strikes-back-for-vision | 12.5 |
| harnessing-hierarchical-label-distribution | 16.74 |
| global-and-local-mixture-consistency-1 | 10.42 |
| test-agnostic-long-tailed-recognition-by-test | 16.2 |
| weight-guided-class-complementing-for-long | 15.4 |
| targeted-supervised-contrastive-learning-for | 21.3 |
| a-unified-generalization-analysis-of-re | 13.58 |
| self-supervised-learning-is-more-robust-to-1 | 14.4 |
| global-and-local-mixture-consistency-1 | 11.50 |
| a-simple-episodic-linear-probe-improves | 22 |
| sure-survey-recipes-for-building-reliable-and | 13.07 |
| improving-calibration-for-long-tailed-1 | 17.9 |
| trustworthy-long-tailed-classification | 19.6 |
| pure-noise-to-the-rescue-of-insufficient-data | 13.9 |
| nested-collaborative-learning-for-long-tailed | 15.3 |
| metasaug-meta-semantic-augmentation-for-long | 19.34 |
| learning-imbalanced-datasets-with-label | 22.97 |