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Unsupervised Few-Shot Learning

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.

Papers

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Showing 11–20 of 22 papers

TitleStatusHype
Unsupervised Learning via Meta-Learning—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Shot in the Dark: Few-Shot Learning with No Base-Class Labels—0
Trainable Class Prototypes for Few-Shot Learning—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps—0
Unsupervised Few Shot Learning via Self-supervised Training—0
Trip-ROMA: Self-Supervised Learning with Triplets and Random MappingsCode0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
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