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Learning disentangled representation from any unlabelled data is anon-trivial problem. In this paper we propose Information MaximisingAutoencoder (InfoAE) where the encoder learns powerful disentangledrepresentation through maximizing the mutual information between therepresentation and given information in an unsupervised fashion. We haveevaluated our model on MNIST dataset and achieved 98.9 ($\pm .1$) $\%$ testaccuracy while using complete unsupervised training.

Disentangled Representation Learning with Information Maximizing Autoencoder