The Potential of Latent Spaces: Diffusion Models for Cross-Domain Generalization

The generalization of models to unknown data distributions is a central challenge in Artificial Intelligence. A promising approach, called domain generalization, aims to develop models that are robust to variations in the data and can thus generalize to new, unseen domains. Recent research investigates how model architectures and pre-training objectives influence the richness of features and proposes a method to effectively utilize them for domain generalization.

The core of the method lies in the utilization of the latent space of diffusion models. Starting from a pre-trained feature space, latent domain structures, called pseudo-domains, are first discovered. These capture domain-specific variations in an unsupervised manner, i.e., without explicit domain labels. Existing classifiers are then augmented with these complementary pseudo-domain representations, enabling them to react more flexibly to diverse unseen test domains.

The study analyzes how different pre-trained feature spaces differ in the captured domain-specific variances. It shows that features from diffusion models are particularly good at separating domains even without explicit domain labels and capturing nuanced domain-specific information. This ability to recognize implicit domain structures is crucial for generalization to unknown domains.

Empirical studies on five different datasets demonstrate the effectiveness of the approach. The simple method improves generalization to unseen domains and achieves a maximum improvement in test accuracy of over 4% compared to the standard baseline Empirical Risk Minimization (ERM). Remarkably, the method even outperforms most algorithms that have access to domain labels during training. This underscores the potential of utilizing latent spaces of diffusion models for domain generalization.

The Significance for AI Applications

The results of this research are promising for diverse AI applications. Through improved domain generalization, models can be deployed more robustly and reliably in real-world scenarios where the data distribution often deviates from the training data. This is particularly relevant for areas such as Computer Vision, Natural Language Processing, and Robotics, where models need to generalize to a variety of environments and data distributions.

The use of diffusion models for extracting latent domain structures opens up new possibilities for the development of AI systems that can adapt to changing conditions and function effectively in complex, real-world environments. Future research could focus on optimizing the method and applying it to further application areas.

Bibliography: - https://arxiv.org/abs/2503.06698 - http://paperreading.club/page?id=290744 - https://openreview.net/forum?id=YveXwFMUr1&referrer=%5Bthe%20profile%20of%20Qi%20Tian%5D(%2Fprofile%3Fid%3D~Qi_Tian3) - https://arxiv.org/abs/2502.02225 - https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/05806.pdf - https://link.springer.com/article/10.1007/s00521-022-07890-2 - https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers - https://ui.adsabs.harvard.edu/abs/arXiv:2312.05387 - https://iclr.cc/virtual/2024/papers.html