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We combine conditional variational autoencoders (VAE) with adversarialcensoring in order to learn invariant representations that are disentangledfrom nuisance/sensitive variations. In this method, an adversarial networkattempts to recover the nuisance variable from the representation, which theVAE is trained to prevent. Conditioning the decoder on the nuisance variableenables clean separation of the representation, since they are recombined formodel learning and data reconstruction. We show this natural approach istheoretically well-founded with information-theoretic arguments. Experimentsdemonstrate that this method achieves invariance while preserving modellearning performance, and results in visually improved performance for styletransfer and generative sampling tasks.

Invariant Representations from Adversarially Censored Autoencoders