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arXiv · 2203.15702

Contrasting the landscape of contrastive and non-contrastive learning

Ashwini Pokle·Jinjin Tian·Yuchen Li·Andrej Risteski
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Abstract

A lot of recent advances in unsupervised feature learning are based on designing features which are invariant under semantic data augmentations. A common way to do this is contrastive learning, which uses positive and negative samples. Some recent works however have shown promising results for non-contrastive learning, which does not require negative samples. However, the non-contrastive losses have obvious "collapsed" minima, in which the encoders output a constant feature embedding, independent of the input. A folk conjecture is that so long as these collapsed solutions are avoided, the produced feature representations should be good. In our paper, we cast doubt on this story: we show through theoretical results and controlled experiments that even on simple data models, non-contrastive losses have a preponderance of non-collapsed bad minima. Moreover, we show that the training process does not avoid these minima.

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Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski. 2022-03-29. Contrasting the landscape of contrastive and non-contrastive learning. https://arxiv.org/abs/2203.15702

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