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Unsupervised Few-Shot Learning
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In contrast to supervised few-shot learning, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot learning.
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BECLR: Batch Enhanced Contrastive Few-Shot Learning
Self-Attention Message Passing for Contrastive Few-Shot Learning
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
Self-Supervision Can Be a Good Few-Shot Learner
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
Trainable Class Prototypes for Few-Shot Learning
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps
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
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
Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning Test
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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