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arXiv · 2006.10803

Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations

Mahmoud Assran·Nicolas Ballas·Lluis Castrejon·Michael Rabbat
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Abstract

We investigate a strategy for improving the efficiency of contrastive learning of visual representations by leveraging a small amount of supervised information during pre-training. We propose a semi-supervised loss, SuNCEt, based on noise-contrastive estimation and neighbourhood component analysis, that aims to distinguish examples of different classes in addition to the self-supervised instance-wise pretext tasks. On ImageNet, we find that SuNCEt can be used to match the semi-supervised learning accuracy of previous contrastive approaches while using less than half the amount of pre-training and compute. Our main insight is that leveraging even a small amount of labeled data during pre-training, and not only during fine-tuning, provides an important signal that can significantly accelerate contrastive learning of visual representations. Our code is available online at github.com/facebookresearch/suncet.

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BibTeXRIS

Mahmoud Assran, Nicolas Ballas, Lluis Castrejon, Michael Rabbat. 2020-06-18. Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations. https://arxiv.org/abs/2006.10803

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