What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization

• URL: http://arxiv.org/abs/2503.06698v2
• Date: Mon, 28 Apr 2025 21:50:29 GMT
• Title: What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization
• Authors: Xavier Thomas, Deepti Ghadiyaram,
• Abstract summary: Domain Generalization aims to develop models that can generalize to novel and unseen data distributions.<n>We study how model architectures and pre-training objectives impact feature richness.<n>Our framework improves generalization to unseen domains by a maximum test accuracy improvement of over 4%.
• Score: 10.079844840768054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain Generalization aims to develop models that can generalize to novel and unseen data distributions. In this work, we study how model architectures and pre-training objectives impact feature richness and propose a method to effectively leverage them for domain generalization. Specifically, given a pre-trained feature space, we first discover latent domain structures, referred to as pseudo-domains, that capture domain-specific variations in an unsupervised manner. Next, we augment existing classifiers with these complementary pseudo-domain representations making them more amenable to diverse unseen test domains. We analyze how different pre-training feature spaces differ in the domain-specific variances they capture. Our empirical studies reveal that features from diffusion models excel at separating domains in the absence of explicit domain labels and capture nuanced domain-specific information. On 5 datasets, we show that our very simple framework improves generalization to unseen domains by a maximum test accuracy improvement of over 4% compared to the standard baseline Empirical Risk Minimization (ERM). Crucially, our method outperforms most algorithms that access domain labels during training.

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