Controlling Directions Orthogonal to a Classifier

• URL: http://arxiv.org/abs/2201.11259v1
• Date: Thu, 27 Jan 2022 01:23:08 GMT
• Title: Controlling Directions Orthogonal to a Classifier
• Authors: Yilun Xu, Hao He, Tianxiao Shen, Tommi Jaakkola
• Abstract summary: We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer. We present three use cases where controlling orthogonal variation is important: style transfer, domain adaptation, and fairness. The code is available at http://github.com/Newbeeer/orthogonal_classifier.
• Score: 11.882219706353045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer. While orthogonal decomposition is directly identifiable when the given classifier is linear, we formally define a notion of orthogonality in the non-linear case. We also provide a surprisingly simple method for constructing the orthogonal classifier (a classifier utilizing directions other than those of the given classifier). Empirically, we present three use cases where controlling orthogonal variation is important: style transfer, domain adaptation, and fairness. The orthogonal classifier enables desired style transfer when domains vary in multiple aspects, improves domain adaptation with label shifts and mitigates the unfairness as a predictor. The code is available at http://github.com/Newbeeer/orthogonal_classifier

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