Papers › Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps

Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps

7 Oct 2022arXiv:2210.03595archive 2025-07-28

Kuilin Chen, Chi-Guhn Lee

Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or meta-learning on labeled meta-training data and cannot be applied to the case where the pretraining data is unlabeled. In this study, we present an unsupervised few-shot learning method via deep Laplacian eigenmaps. Our method learns representation from unlabeled data by grouping similar samples together and can be intuitively interpreted by random walks on augmented training data. We analytically show how deep Laplacian eigenmaps avoid collapsed representation in unsupervised learning without explicit comparison between positive and negative samples. The proposed method significantly closes the performance gap between supervised and unsupervised few-shot learning. Our method also achieves comparable performance to current state-of-the-art self-supervised learning methods under linear evaluation protocol.

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Tasks

Few-Shot Learning Linear evaluation Meta-Learning Self-Supervised Learning Unsupervised Few-Shot Image Classification Unsupervised Few-Shot Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Unsupervised Few-Shot Image Classification	Mini-Imagenet 5-way (1-shot)	Deep Laplacian Eigenmaps	Accuracy	59.47	#6 of 28	Archive leaderboard	report
Unsupervised Few-Shot Image Classification	Mini-Imagenet 5-way (5-shot)	Deep Laplacian Eigenmaps	Accuracy	78.79	#5 of 28	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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