Unsupervised Few Shot Image Classification On

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Accuracy

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title
BECLR	80.57	BECLR: Batch Enhanced Contrastive Few-Shot Learning
Meta-DM+UniSiam	66.68	Meta-DM: Applications of Diffusion Models on Few-Shot Learning
UniSiam	65.55	Self-Supervision Can Be a Good Few-Shot Learner
PDA-Net	63.84	Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
SAMPTransfer (Conv4)	61.02	Self-Attention Message Passing for Contrastive Few-Shot Learning
Deep Laplacian Eigenmaps	59.47	Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
TrainProto	58.92	Trainable Class Prototypes for Few-Shot Learning
HMS	58.20	Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
UBC-FSL	57.1	Shot in the Dark: Few-Shot Learning with No Base-Class Labels
CSSL	54.17	Few-Shot Image Classification via Contrastive Self-Supervised Learning
CPNWCP	53.56	Contrastive Prototypical Network with Wasserstein Confidence Penalty
LF2CS	53.14	Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
PL-CFE	49.13	Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
C^3LR	47.92	Self-Supervised Class-Cognizant Few-Shot Classification
PsCo	46.70	Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
AmdimNet	46.13	Self-Supervised Learning For Few-Shot Image Classification
ProtoTransfer	45.67	Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
CMVAE	44.27	CMVAE: Causal Meta VAE for Unsupervised Meta-Learning
Meta-SVEBM	43.38	Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling
Meta-GMVAE	42.82	Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning

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Unsupervised Few Shot Image Classification On

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