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Constrained Maximum Cross-Domain Likelihood for Domain Generalization.

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    This study introduces a novel domain generalization method using Kullback-Leibler divergence to align distributions across domains. The approach enhances model generalizability for improved performance on unseen data.

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    Area of Science:

    • Machine Learning
    • Computer Vision

    Background:

    • Domain generalization (DG) aims to create models that perform well on unseen domains by training on multiple source domains.
    • Current DG methods often align distributions across domains but struggle with relaxed conditions and achieving joint distribution alignment.
    • Existing techniques face challenges like entropy increase and the unavailability of ground-truth marginal distributions.

    Purpose of the Study:

    • To propose a novel domain generalization method that learns a domain-invariant classifier by minimizing Kullback-Leibler (KL)-divergence between posterior distributions.
    • To address deficiencies in KL-divergence, such as entropy increase and unavailable marginal distributions, thereby enhancing classifier generalizability.
    • To achieve natural joint distribution alignment through a constrained maximum cross-domain likelihood (CMCL) optimization problem.

    Main Methods:

    • Minimizing the KL-divergence between posterior distributions from different domains to learn a domain-invariant classifier.
    • Introducing maximum in-domain likelihood to counteract entropy increase and maintain representation space discrimination.
    • Approximating ground-truth marginal distributions using source domains under a convex hull assumption.
    • Developing a constrained maximum cross-domain likelihood (CMCL) optimization problem and an alternating optimization strategy.

    Main Results:

    • The proposed method successfully aligns joint distributions across domains.
    • Experiments on Digits-DG, PACS, Office-Home, and miniDomainNet datasets demonstrate superior performance compared to existing methods.
    • The CMCL optimization, solved via alternating optimization, effectively addresses the identified challenges in domain generalization.

    Conclusions:

    • The novel domain generalization method effectively learns domain-invariant features and classifiers.
    • The approach overcomes limitations of previous methods by addressing entropy increase and marginal distribution unavailability.
    • The proposed technique achieves state-of-the-art performance on standard benchmark datasets, highlighting its practical utility.