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.
@article{arxiv.2006.10803,
title = {Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations},
author = {Mahmoud Assran and Nicolas Ballas and Lluis Castrejon and Michael Rabbat},
journal= {arXiv preprint arXiv:2006.10803},
year = {2020}
}