Papers › Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Puneet Mangla, Mayank Singh, Abhishek Sinha, Nupur Kumari, Vineeth N. Balasubramanian, Balaji Krishnamurthy
Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution. Since the goal of few-shot learning is closely linked to robust representation learning, we study Manifold Mixup in this problem setting. Self-supervised learning is another technique that learns semantically meaningful features, using only the inherent structure of the data. This work investigates the role of learning relevant feature manifold for few-shot tasks using self-supervision and regularization techniques. We observe that regularizing the feature manifold, enriched via self-supervised techniques, with Manifold Mixup significantly improves few-shot learning performance. We show that our proposed method S2M2 beats the current state-of-the-art accuracy on standard few-shot learning datasets like CIFAR-FS, CUB, mini-ImageNet and tiered-ImageNet by 3-8 %. Through extensive experimentation, we show that the features learned using our approach generalize to complex few-shot evaluation tasks, cross-domain scenarios and are robust against slight changes to data distribution.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | CIFAR-FS 5-way (1-shot) | S2M2R | Accuracy | 74.81 | #26 of 38 | Archive leaderboard | report |
| Few-Shot Image Classification | CIFAR-FS 5-way (5-shot) | S2M2R | Accuracy | 87.47 | #23 of 39 | Archive leaderboard | report |
| Few-Shot Image Classification | CUB 200 5-way 1-shot | S2M2R | Accuracy | 80.68 | #19 of 36 | Archive leaderboard | report |
| Few-Shot Image Classification | CUB 200 5-way 5-shot | S2M2R | Accuracy | 90.85 | #18 of 32 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | S2M2R | Accuracy | 64.93 | #59 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | S2M2R | Accuracy | 83.18 | #37 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | S2M2R | Accuracy | 73.71 | #23 of 49 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | S2M2R | Accuracy | 88.59 | #13 of 51 | 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.
Methods
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