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算力平台

Unsupervised Few Shot Image Classification On 1

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

Paper Title
BECLR87.82BECLR: Batch Enhanced Contrastive Few-Shot Learning
Meta-DM+UniSiam85.29Meta-DM: Applications of Diffusion Models on Few-Shot Learning
UniSiam83.40Self-Supervision Can Be a Good Few-Shot Learner
PDA-Net83.11Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
Deep Laplacian Eigenmaps78.79Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
UBC-FSL77.2Shot in the Dark: Few-Shot Learning with No Base-Class Labels
HMS75.77Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
TrainProto73.94Trainable Class Prototypes for Few-Shot Learning
CPNWCP73.21Contrastive Prototypical Network with Wasserstein Confidence Penalty
SAMPTransfer (Conv4)72.52Self-Attention Message Passing for Contrastive Few-Shot Learning
AmdimNet70.14Self-Supervised Learning For Few-Shot Image Classification
CSSL68.91Few-Shot Image Classification via Contrastive Self-Supervised Learning
LF2CS67.36Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
C^3LR64.81Self-Supervised Class-Cognizant Few-Shot Classification
PsCo63.26Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
ProtoTransfer62.99Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
PL-CFE62.91Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
CMVAE58.95CMVAE: Causal Meta VAE for Unsupervised Meta-Learning
Meta-SVEBM58.03Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling
ArL57.01Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
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