SwAV learns visual representations by simultaneously clustering data and enforcing consistency between cluster assignments produced for different augmentations of the same image. The method uses a swapped prediction mechanism where it predicts the cluster assignment of one view from the representation of another. It provides pre-trained ResNet-50 models and can be trained with both large and small batches, scaling to unlimited amounts of unlabeled data.