Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning

Abstract

We are interested in representation learning in self-supervised, supervised, or semi-supervised settings. The prior work on applying mean-shift idea for self-supervised learning, MSF, generalizes the BYOL idea by pulling a query image to not only be closer to its other augmentation, but also to the nearest neighbors (NNs) of its other augmentation. We believe the learning can benefit from choosing far away neighbors that are still semantically related to the query. Hence, we propose to generalize MSF algorithm by constraining the search space for nearest neighbors. We show that our method outperforms MSF in SSL setting when the constraint utilizes a different augmentation of an image, and outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures the NNs have the same pseudo-label as the query.