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Unsupervised image representations have significantly reduced the gap withsupervised pretraining, notably with the recent achievements of contrastivelearning methods. These contrastive methods typically work online and rely on alarge number of explicit pairwise feature comparisons, which is computationallychallenging. In this paper, we propose an online algorithm, SwAV, that takesadvantage of contrastive methods without requiring to compute pairwisecomparisons. Specifically, our method simultaneously clusters the data whileenforcing consistency between cluster assignments produced for differentaugmentations (or views) of the same image, instead of comparing featuresdirectly as in contrastive learning. Simply put, we use a swapped predictionmechanism where we predict the cluster assignment of a view from therepresentation of another view. Our method can be trained with large and smallbatches and can scale to unlimited amounts of data. Compared to previouscontrastive methods, our method is more memory efficient since it does notrequire a large memory bank or a special momentum network. In addition, we alsopropose a new data augmentation strategy, multi-crop, that uses a mix of viewswith different resolutions in place of two full-resolution views, withoutincreasing the memory or compute requirements much. We validate our findings byachieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well assurpassing supervised pretraining on all the considered transfer tasks.

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments