Self-Supervised Learning For Few-Shot Image Classification

TL;DR

This paper introduces a self-supervised learning approach to train a generalized embedding network for few-shot image classification, outperforming existing methods and demonstrating robustness across multiple datasets.

Contribution

The paper proposes using self-supervised learning to improve embedding networks for few-shot classification, addressing the limitations of supervised training with limited data.

Findings

Achieves state-of-the-art results on MiniImageNet and CUB datasets.

Demonstrates robustness in cross-domain few-shot learning.

Outperforms baseline methods in accuracy.

Abstract

Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing. Due to the limited number of samples for each task, the initial embedding network for meta-learning becomes an essential component and can largely affect the performance in practice. To this end, most of the existing methods highly rely on the efficient embedding network. Due to the limited labelled data, the scale of embedding network is constrained under a supervised learning(SL) manner which becomes a bottleneck of the few-shot learning methods. In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself. We evaluate our work by extensive comparisons with previous baseline methods on two few-shot classification datasets ({\em i.e.,} MiniImageNet and CUB) and achieve better performance over baselines. Tests on four datasets in cross-domain few-shot learning classification show that the proposed method achieves state-of-the-art results and further prove the robustness of the proposed model. Our code is available at \hyperref[https://github.com/phecy/SSL-FEW-SHOT.]{https://github.com/phecy/SSL-FEW-SHOT.}