  We tackle the problem disentangling the latent space of an autoencoder in
order to separate labelled attribute information from other characteristic
information. This then allows us to change selected attributes while preserving
other information. Our method, matrix subspace projection, is much simpler than
previous approaches to latent space factorisation, for example not requiring
multiple discriminators or a careful weighting among their loss functions.
Furthermore our new model can be applied to autoencoders as a plugin, and works
across diverse domains such as images or text. We demonstrate the utility of
our method for attribute manipulation in autoencoders trained across varied
domains, using both human evaluation and automated methods. The quality of
generation of our new model (e.g. reconstruction, conditional generation) is
highly competitive to a number of strong baselines.
