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 and mini-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.

本文研究了Manifold Mixup在few-shot学习中的作用，并结合自监督学习和正则化技术，提出了一种名为S2M2的方法，显著提高了few-shot学习的性能，并在CIFAR-FS、CUB、mini-ImageNet和tiered-ImageNet等标准Few-shot Learning数据集上打破了当前最新的准确性记录。

正确绘制流形图：用于小样本学习的Manifold Mixup