This paper proposes a novel method for factorising the information in the latent space of an autoencoder (AE), to improve the interpretability of the latent space and facilitate controlled generation. When trained on a dataset with labelled attributes we can produce a latent vector which separates information encoding the attributes from other characteristic information, and also disentangles the attribute information. This then allows us to manipulate each attribute of the latent representation individually without affecting others. Our method, matrix subspace projection, is simpler than the state of the art adversarial network approaches to latent space factorisation. We demonstrate the utility of the method for attribute manipulation tasks on the CelebA image dataset and the E2E text corpus.
@cite_18: We present a method for training variational autoencoders on labelled datasets which encode information corresponding to the labels in explicitly predetermined subspaces of the latent space. We motivate our model from both an information theoretic perspective as well as a adversarial game perspective. By separating labelled information into a less complicated space we allow the model to more easily disentangle representations. This provides a form of semi-supervised learning of attributes. Since these subspaces can be chosen a priori, setting them to be low-dimensional provides a form of dimensionality reduction. We demonstrate the utility of our model on attribute manipulation tasks with several image datasets.
@cite_18 , also adversarial, take a slightly different approach in training a multi-dimensional space for an attribute, rather than a single dimension. In contrast to our focus on disentanglement, their focus is on allowing control of a larger variety in generation, for example, with glasses: a variety of rims and different styles of sunglasses', or with facial hair: both moustaches and beards of varying thickness'. They focused on two subsets of the CelebA dataset to train for the attributes glasses and facial hair, and their combination, but not simultaneously for all the 40 labels available.