Disentangling Factors of Variation by Mixing Them
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We propose an unsupervised approach to learn image representations that consist of disentangled factors of variation. A factor of variation corresponds to an image attribute that can be discerned consistently across a set of images, such as the pose or color of objects. Our disentangled representation consists of a concatenation of feature chunks, each chunk representing a factor of variation. It supports applications such as transferring attributes from one image to another, by simply swapping feature chunks, and classification or retrieval based on one or several attributes, by considering a user specified subset of feature chunks. We learn our representation in an unsupervised manner, without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively, and second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding the shortcut where chunks are ignored completely. We demonstrate the effectiveness of our approach on the MNIST, Sprites, and CelebA datasets.
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