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Divide and Contrast: Self-supervised Learning from Uncurated Data

2021-05-17 17:59:03
Yonglong Tian, Olivier J. Henaff, Aaron van den Oord

Abstract

Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data such as ImageNet. We explore the effects of contrastive learning from larger, less-curated image datasets such as YFCC, and find there is indeed a large difference in the resulting representation quality. We hypothesize that this curation gap is due to a shift in the distribution of image classes -- which is more diverse and heavy-tailed -- resulting in less relevant negative samples to learn from. We test this hypothesis with a new approach, Divide and Contrast (DnC), which alternates between contrastive learning and clustering-based hard negative mining. When pretrained on less curated datasets, DnC greatly improves the performance of self-supervised learning on downstream tasks, while remaining competitive with the current state-of-the-art on curated datasets.

Abstract (translated)

URL

https://arxiv.org/abs/2105.08054

PDF

https://arxiv.org/pdf/2105.08054.pdf


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