Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution. There has also been increased interest in disentangling the internal representations learned by deep methods to further improve their performance and achieve a finer control. In this paper, we bridge these two objectives and introduce the concept of cross-domain disentanglement. We aim to separate the internal representation into three parts. The shared part contains information for both domains.

Neural Information Processing Systems http://nips.cc/

We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed.

AI systems can take harmful actions and are highly vulnerable to adversarial attacks.

Neural Information Processing Systems http://nips.cc/

Perception, in theoretical neuroscience, has been modeled as the encoding of external stimuli into internal signals, which are then decoded.