  Recently, contrastive learning has achieved great results in self-supervised
learning, where the main idea is to push two augmentations of an image
(positive pairs) closer compared to other random images (negative pairs). We
argue that not all random images are equal. Hence, we introduce a self
supervised learning algorithm where we use a soft similarity for the negative
images rather than a binary distinction between positive and negative pairs. We
iteratively distill a slowly evolving teacher model to the student model by
capturing the similarity of a query image to some random images and
transferring that knowledge to the student. We argue that our method is less
constrained compared to recent contrastive learning methods, so it can learn
better features. Specifically, our method should handle unbalanced and
unlabeled data better than existing contrastive learning methods, because the
randomly chosen negative set might include many samples that are semantically
similar to the query image. In this case, our method labels them as highly
similar while standard contrastive methods label them as negative pairs. Our
method achieves better results compared to state-of-the-art models like BYOL
and MoCo on transfer learning settings. We also show that our method performs
better in the settings where the unlabeled data is unbalanced. Our code is
available here: https://github.com/UMBCvision/ISD.
