Long Tail Learning On Cifar 100 Lt R 100

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Error Rate

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

		Paper Title
Cross-Entropy (CE)	62.75	Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics
Cross-Entropy (CE)	61.68	Class-Balanced Loss Based on Effective Number of Samples
IBLLoss	61.52	Influence-Balanced Loss for Imbalanced Visual Classification
Cross-Entropy + Curvature Regularization	59.5	Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
CE-DRW	58.9	The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
LDAM-DRW	57.96	Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
ELP	57.6	A Simple Episodic Linear Probe Improves Visual Recognition in the Wild
CDB-loss	57.43	Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance
CE-DRW-IC	56.9	Posterior Re-calibration for Imbalanced Datasets
LDAM-DRW + SSP	56.57	Rethinking the Value of Labels for Improving Class-Imbalanced Learning
smDRAGON	56.50	From Generalized zero-shot learning to long-tail with class descriptors
LDAM-DRW + WGCC	56.4	Weight-guided class complementing for long-tailed image recognition
TSC(ResNet-32)	56.2	Targeted Supervised Contrastive Learning for Long-Tailed Recognition
CE+DRS+GIT	55.65	Do Deep Networks Transfer Invariances Across Classes?
LDAM-DRW-RSG	55.5	RSG: A Simple but Effective Module for Learning Imbalanced Datasets
Hybrid-PSC	55.03	Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
FBL (Resnet-32)	54.78	Feature-Balanced Loss for Long-Tailed Visual Recognition
ETF Classifier + DR (Resnet)	54.7	Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
LADE	54.6	Disentangling Label Distribution for Long-tailed Visual Recognition
UniMix+Bayias (ResNet-32)	54.55	Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective

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Long Tail Learning On Cifar 100 Lt R 100

評価指標

評価結果