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[R] Weakly Supervised Disentanglement with Guarantees

We build a theoretical framework for analyzing disentanglement in the weakly supervised regime. We provide new definitions for disentanglement (sorry) that can be measured in a weakly supervised manner, and use these definitions as the cornerstone for developing a calculus and theory of disentanglement. We then analyzed several weak supervision techniques and proved (and empirically demonstrated) their disentanglement guarantees (or lack thereof).

We hope that the concepts developed in this paper will help researchers frame their discussion and analysis of weakly supervised disentanglement in future work.

Paper: https://arxiv.org/abs/1910.09772

Cute gif: https://twitter.com/i/status/1187507675258486784

submitted by /u/approximately_wrong
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.