Invariant Representations from Adversarially Censored Autoencoders
This work addresses the need for robust representation learning in machine learning by providing a method to separate sensitive variations, though it is incremental as it builds on existing VAE and adversarial techniques.
The paper tackled the problem of learning invariant representations disentangled from nuisance variations by combining conditional variational autoencoders with adversarial censoring, resulting in improved performance for style transfer and generative sampling tasks.
We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variations. In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent. Conditioning the decoder on the nuisance variable enables clean separation of the representation, since they are recombined for model learning and data reconstruction. We show this natural approach is theoretically well-founded with information-theoretic arguments. Experiments demonstrate that this method achieves invariance while preserving model learning performance, and results in visually improved performance for style transfer and generative sampling tasks.