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Unsupervised Few-Shot Image Classification
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In contrast to (supervised) few-shot image classification, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot image classification.
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Meta-DM: Applications of Diffusion Models on Few-Shot Learning
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
CMVAE: Causal Meta VAE for Unsupervised Meta-Learning
Self-Attention Message Passing for Contrastive Few-Shot Learning
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
Self-Supervision Can Be a Good Few-Shot Learner
Self-Supervised Class-Cognizant Few-Shot Classification
Multi-level Second-order Few-shot Learning
Trainable Class Prototypes for Few-Shot Learning
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
Shot in the Dark: Few-Shot Learning with No Base-Class Labels
Few-Shot Image Classification via Contrastive Self-Supervised Learning
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
Unsupervised Few-shot Learning via Self-supervised Training
Self-Supervised Learning For Few-Shot Image Classification
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation
Unsupervised Meta-Learning For Few-Shot Image Classification
Unsupervised Learning via Meta-Learning
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