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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 1–22 of 22 papers

TitleStatusHype
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-RepsCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-LearningCode1
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot LearningCode1
Self-Attention Message Passing for Contrastive Few-Shot LearningCode1
Self-Supervised Prototypical Transfer Learning for Few-Shot ClassificationCode1
Self-Supervision Can Be a Good Few-Shot LearnerCode1
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data AugmentationCode1
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning TestCode0
Trip-ROMA: Self-Supervised Learning with Triplets and Random MappingsCode0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Trainable Class Prototypes for Few-Shot Learning—0
Unsupervised Few-shot Learning via Self-supervised Training—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
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
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
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