Self-Distilled Self-Supervised Representation Learning

TL;DR

This paper introduces SDSSL, a self-distillation approach in self-supervised learning that enhances intermediate and final layer representations in transformer models, leading to improved performance across tasks.

Contribution

It proposes a novel self-distillation method for self-supervised learning that improves intermediate layer representations and overall model performance.

Findings

Outperforms SimCLR, BYOL, MoCo v3 on various datasets.

Enhances representations in both final and lower layers.

Leads to better instance discrimination and easier pretext tasks.

Abstract

State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN models. Striving to maximize the mutual information of two views of an image, existing works apply a contrastive loss to the final representations. Motivated by self-distillation in the supervised regime, we further exploit this by allowing the intermediate representations to learn from the final layer via the contrastive loss. Through self-distillation, the intermediate layers are better suited for instance discrimination, making the performance of an early-exited sub-network not much degraded from that of the full network. This renders the pretext task easier also for the final layer, leading to better representations. Our method, Self-Distilled Self-Supervised Learning (SDSSL), outperforms competitive baselines (SimCLR, BYOL and MoCo v3) using ViT on various tasks and datasets. In the linear evaluation and k-NN protocol, SDSSL not only leads to superior performance in the final layers, but also in most of the lower layers. Furthermore, qualitative and quantitative analyses show how representations are formed more effectively along the transformer layers. Code is available at https://github.com/hagiss/SDSSL.