This work is on the task of self supervised learning of image representations. The main objective is to learn representations that best represents an image in a lower dimensional latent space without losing much information. Our approach builds on top of the Deep InfoMax (DIM) algorithm that is based on the concept of mutual information maximization. In addition to this, our method imposes further restriction on the learned representations by training an additional classifier to predict the self-imposed rotations on input images.
