Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Understand

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.