Disentangled Representation Learning with Information Maximizing Autoencoder
Learning disentangled representation from any unlabelled data is a non-trivial problem.In this paper we propose Information Maximising Autoencoder 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 approximately 98.9 % test accuracy while using complete unsupervised training.
Learn disentangle representation in an unsupervised manner.The authors present a framework in which an auto encoder (E, D) is regularized such that its latent representation to share mutual information with a generated latent space representation.