Disentangled Representation Learning with Information Maximizing Autoencoder

Learning disentangled representation from any unlabelled data is a nontrivial problem. In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion. We have evaluated our model on MNIST dataset and achieved 98.9 ( .1) Learning disentangled representation from any unlabelled data is an active area of research [1]. Self supervised learning [2, 3, 4] is a way to learn representation from the unlabelled data but the supervised signal is needed to be developed manually, which usually varies depending on the problem and the dataset. Generative Adversarial Neural Networks (GANs) [5] is a potential candidate for learning disentangled representation from unlabelled data ([6, 7, 8]).