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SOTA
Long Tail Learning
Long Tail Learning On Cifar 100 Lt R 50
Long Tail Learning On Cifar 100 Lt R 50
평가 지표
Error Rate
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Error Rate
Paper Title
Repository
DeiT-LT
39.5
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
BCL(ResNet-32)
43.4
Balanced Contrastive Learning for Long-Tailed Visual Recognition
ConCutMix
-
Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation
MetaSAug-LDAM
47.73
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
LDAM-DRW + SSP
52.89
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
LIFT (ViT-B/16, CLIP)
16.9
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
GCL
46.4
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
PC
42.25
Learning Prototype Classifiers for Long-Tailed Recognition
Difficulty-Net
43.1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
LDAM-DRW-RSG
51.5
RSG: A Simple but Effective Module for Learning Imbalanced Datasets
GLMC (ResNet-34, channel x4)
36.15
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
LIFT (ViT-B/16, ImageNet-21K pre-training)
9.8
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
GLMC + SAM
34.72
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
OPeN (WideResNet-28-10)
40.2
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
MiSLAS
47.7
Improving Calibration for Long-Tailed Recognition
NCL(ResNet32)
43.2
Nested Collaborative Learning for Long-Tailed Visual Recognition
-
TADE
46.1
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
-
Hybrid-PSC
51.07
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
-
CBD+TailCalibX
49.1
Feature Generation for Long-tail Classification
GML (ResNet-32)
41.9
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
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