Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the code of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.

Weaknesses: The paper has many weak points unfortunately. They are presented below as separate categories. Intro/Motivation: The paper focuses too much on "not using momentum encoder", "not using memory bank". All these are largely irrelevant points. Firstly, until one shows one gets no benefit from momentum encoder, it is best not to claim that "not having momentum" is a contribution / a positive aspect of the model.

The paper makes two incremental contributions in using online cluster assignments in self-supervised learning and using multiple crops in different resolutions for data augmentation. When these contributions are combined, decent gains in classification accuracy are obtained. The reviewers raise many issues with the current manuscript, including the discussion of momentum encoder, the discussion of existing clustering-based approaches, and the potential misuse of the term clustering. I ask the authors to incorporate all of these comments in the final version, but I believe the contributions even though incremental in nature, can benefit the fast growing field of self-supervised learning.

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the code of a view from the representation of another view.