title: What's in a Latent? Leveraging Diffusion Latent Space for Domain
    Generalization
summary: 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.
paper_authors._id: 67cf9d9ebe9366d1ae96de5e
paper_authors.name: Xavier Thomas
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T08:42:42.540Z
paper_authors._id: 6505b787dacc94cd6ce9d256
paper_authors.avatarUrl: /avatars/9490807f97afc39aeb663d343a900ff9.svg
paper_authors.fullname: Thomas
paper_authors.type: user
paper_authors.user: XThomasBU
paper_authors._id: 67cf9d9ebe9366d1ae96de5f
paper_authors.name: Deepti Ghadiyaram
paper_title: What's in a Latent? Leveraging Diffusion Latent Space for Domain
    Generalization
paper_summary: 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.
paper_ai_summary: A method to improve domain generalization by leveraging latent domain structures (pseudo-domains) from pre-trained feature spaces, especially diffusion models, enhances classifier performance on unseen domains.
paper_ai_keywords: domain generalization model architectures pre-training objectives feature richness latent domain structures pseudo-domains domain-specific variations diffusion models empirical risk minimization (ERM)