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

## TL;DR

This paper demonstrates that combining self-supervised learning with Manifold Mixup significantly enhances few-shot learning performance by improving feature robustness and generalization across various datasets and scenarios.

## Contribution

The study introduces S2M2, a novel approach that integrates self-supervision with Manifold Mixup, achieving state-of-the-art results in few-shot learning tasks.

## Key findings

- S2M2 outperforms existing methods by 3-8% on standard datasets.
- Features learned are robust to data distribution shifts.
- Method generalizes well to cross-domain and complex tasks.

## Abstract

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.