Deep Bregman Divergence for Contrastive Learning of Visual Representations

Deep Bregman divergence measures divergence of data points using neural networks which is beyond Euclidean distance and capable of capturing divergence over distributions. In this paper, we propose deep Bregman divergences for contrastive learning of visual representation and we aim to enhance contrastive loss used in self-supervised learning by training additional networks based on functional Bregman divergence. In contrast to the conventional contrastive learning methods which are solely based on divergences between single points, our framework can capture the divergence between distributions which improves the quality of learned representation. By combining conventional contrastive loss with the proposed divergence loss, our method outperforms baseline and most of previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets. The source code of the method and of all the experiments are available at supplementary.