Towards Principled Disentanglement for Domain Generalization

Document Type

Article

Publication Title

arXiv

Abstract

A fundamental challenge for machine learning models is generalizing to out-of-distribution (OOD) data, in part due to spurious correlations. To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG). We relax this non-trivial constrained optimization problem to a tractable form with finite-dimensional parameterization and empirical approximation. Then a theoretical analysis of the extent to which the above transformations deviates from the original problem is provided. Based on the transformation, we propose a primal-dual algorithm for joint representation disentanglement and domain generalization. In contrast to traditional approaches based on domain adversarial training and domain labels, DDG jointly learns semantic and variation encoders for disentanglement, enabling flexible manipulation and augmentation on training data. DDG aims to learn intrinsic representations of semantic concepts that are invariant to nuisance factors and generalizable across domains. Comprehensive experiments on popular benchmarks show that DDG can achieve competitive OOD performance and uncover interpretable salient structures within data. Copyright © 2021, The Authors. All rights reserved.

DOI

10.48550/arXiv.2111.13839

Publication Date

11-27-2021

Keywords

Benchmarking, Machine learning, Semantics, Constrained domain, Constrained optimi-zation problems, Dimensional parameterization, Empirical approximations, Finite dimensional, Generalisation, Learn+, Machine learning models, Non-trivial, Primal-dual algorithms, Constrained optimization, Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG)

Comments

IR Deposit conditions: non-described

Preprint: arXiv

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