Papers › Unsupervised Meta-Learning For Few-Shot Image Classification

Unsupervised Meta-Learning For Few-Shot Image Classification

28 Nov 2018NeurIPS 2019 12arXiv:1811.11819archive 2025-07-28

Siavash Khodadadeh, Ladislau Bölöni, Mubarak Shah

Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the target task. In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning for classification tasks. The meta-learning step of UMTRA is performed on a flat collection of unlabeled images. While we assume that these images can be grouped into a diverse set of classes and are relevant to the target task, no explicit information about the classes or any labels are needed. UMTRA uses random sampling and augmentation to create synthetic training tasks for meta-learning phase. Labels are only needed at the final target task learning step, and they can be as little as one sample per class. On the Omniglot and Mini-Imagenet few-shot learning benchmarks, UMTRA outperforms every tested approach based on unsupervised learning of representations, while alternating for the best performance with the recent CACTUs algorithm. Compared to supervised model-agnostic meta-learning approaches, UMTRA trades off some classification accuracy for a reduction in the required labels of several orders of magnitude.

Paper PDF Conference PDF

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

No code repository is listed for this paper in the archive or in Syntology's graph.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Classification Few-Shot Image Classification Few-Shot Learning General Classification Image Classification Inductive Bias Meta-Learning One-Shot Learning Unsupervised Few-Shot Image Classification Unsupervised Few-Shot Learning Video Classification image-classification

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)	UMTRA	Accuracy	39.93	#26 of 28	Archive leaderboard	report
Unsupervised Few-Shot Image Classification	Mini-Imagenet 5-way (5-shot)	UMTRA	Accuracy	50.73	#27 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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections