Distilling Visual Priors from Self-Supervised Learning

Bingchen Zhao; Xin Wen

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arXiv · 2008.00261

Distilling Visual Priors from Self-Supervised Learning

Bingchen Zhao·Xin Wen

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Abstract

Convolutional Neural Networks (CNNs) are prone to overfit small training datasets. We present a novel two-phase pipeline that leverages self-supervised learning and knowledge distillation to improve the generalization ability of CNN models for image classification under the data-deficient setting. The first phase is to learn a teacher model which possesses rich and generalizable visual representations via self-supervised learning, and the second phase is to distill the representations into a student model in a self-distillation manner, and meanwhile fine-tune the student model for the image classification task. We also propose a novel margin loss for the self-supervised contrastive learning proxy task to better learn the representation under the data-deficient scenario. Together with other tricks, we achieve competitive performance in the VIPriors image classification challenge.

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BibTeX RIS

Bingchen Zhao, Xin Wen. 2020-08-01. Distilling Visual Priors from Self-Supervised Learning. https://arxiv.org/abs/2008.00261

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