Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks

TL;DR

This paper introduces a novel dataset distillation method for self-supervised pre-training of deep networks, leveraging knowledge distillation to generate small synthetic datasets that improve downstream task performance with limited labeled data.

Contribution

It presents the first effective dataset distillation approach for SSL pre-training, addressing high gradient variance by using knowledge distillation techniques.

Findings

Achieves up to 13% higher accuracy on downstream tasks.

Successfully pre-trains high-quality encoders with synthetic datasets.

Addresses the high variance problem in SSL-based dataset distillation.

Abstract

Dataset distillation (DD) generates small synthetic datasets that can efficiently train deep networks with a limited amount of memory and compute. Despite the success of DD methods for supervised learning, DD for self-supervised pre-training of deep models has remained unaddressed. Pre-training on unlabeled data is crucial for efficiently generalizing to downstream tasks with limited labeled data. In this work, we propose the first effective DD method for SSL pre-training. First, we show, theoretically and empirically, that naive application of supervised DD methods to SSL fails, due to the high variance of the SSL gradient. Then, we address this issue by relying on insights from knowledge distillation (KD) literature. Specifically, we train a small student model to match the representations of a larger teacher model trained with SSL. Then, we generate a small synthetic dataset by matching the training trajectories of the student models. As the KD objective has considerably lower variance than SSL, our approach can generate synthetic datasets that can successfully pre-train high-quality encoders. Through extensive experiments, we show that our distilled sets lead to up to 13% higher accuracy than prior work, on a variety of downstream tasks, in the presence of limited labeled data. Code at https://github.com/BigML-CS-UCLA/MKDT.