Long Tail Learning On Cifar 10 Lt R 100

Error Rate

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	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