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 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.
Li, X., Lin, C., Li, R., Wang, C., & Guerin, F. (Accepted/In press). Latent Space Factorisation and Manipulation via Matrix Subspace Projection. Proceedings of Machine Learning Research, 119. https://proceedings.icml.cc/book/3538.pdf