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Unsupervised Few Shot Image Classification On 3

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
UniSiam86.51Self-Supervision Can Be a Good Few-Shot Learner-
UBC-FSL84.3Shot in the Dark: Few-Shot Learning with No Base-Class Labels-
ArL58.56Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning-
PL-CFE64.31Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning-
ULDA56.78Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation-
U-MlSo57.53Multi-level Second-order Few-shot Learning-
SAMPTransfer (Conv4)65.19Self-Attention Message Passing for Contrastive Few-Shot Learning-
BECLR87.86BECLR: Batch Enhanced Contrastive Few-Shot Learning-
HMS75.85Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks-
LF2CS66.59Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
PDA-Net84.20Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images-
CPNWCP62.96Contrastive Prototypical Network with Wasserstein Confidence Penalty
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Unsupervised Few Shot Image Classification On 3 | SOTA | HyperAI