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  4. Unsupervised Few Shot Image Classification On 2

Unsupervised Few Shot Image Classification On 2

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称
Accuracy
Paper TitleRepository
BECLR81.69BECLR: Batch Enhanced Contrastive Few-Shot Learning-
UniSiam69.60Self-Supervision Can Be a Good Few-Shot Learner-
LF2CS53.16Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
UBC-FSL68.0Shot in the Dark: Few-Shot Learning with No Base-Class Labels-
HMS58.42Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks-
ULDA41.77Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation-
SAMPTransfer (Conv4)49.10Self-Attention Message Passing for Contrastive Few-Shot Learning-
PL-CFE49.51Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning-
ArL43.68Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning-
PDA-Net69.01Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images-
U-MlSo43.01Multi-level Second-order Few-shot Learning-
CPNWCP45.00Contrastive Prototypical Network with Wasserstein Confidence Penalty
0 of 12 row(s) selected.
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