Papers › Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
Han-Jia Ye, Lu Han, De-Chuan Zhan
Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are constructed with data augmentations from unlabeled base classes during meta-training, and we apply embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test. We observe two elements play important roles in UML, i.e., the way to sample tasks and measure similarities between instances. Thus we obtain a strong baseline with two simple modifications -- a sufficient sampling strategy constructing multiple tasks per episode efficiently together with a semi-normalized similarity. We then take advantage of the characteristics of tasks from two directions to get further improvements. First, synthesized confusing instances are incorporated to help extract more discriminative embeddings. Second, we utilize an additional task-specific embedding transformation as an auxiliary component during meta-training to promote the generalization ability of the pre-adapted embeddings. Experiments on few-shot learning benchmarks verify that our approaches outperform previous UML methods and achieve comparable or even better performance than its supervised variants.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
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) | HMS | Accuracy | 58.20 | #8 of 28 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | HMS | Accuracy | 75.77 | #7 of 28 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | HMS | Accuracy | 58.42 | #5 of 12 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | HMS | Accuracy | 75.85 | #5 of 12 | 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