潛在空間有何奧秘？利用擴散潛在空間實現領域泛化

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

March 9, 2025

作者: Xavier Thomas, Deepti Ghadiyaram

cs.AI

摘要

領域泛化旨在開發能夠適應新穎且未見過數據分佈的模型。在本研究中，我們探討了模型架構和預訓練目標如何影響特徵豐富性，並提出了一種有效利用這些特徵進行領域泛化的方法。具體而言，給定一個預訓練的特徵空間，我們首先以無監督的方式發現捕捉領域特定變化的潛在領域結構，稱之為偽域。接著，我們利用這些互補的偽域表示來增強現有的分類器，使其更適應多樣化的未見過測試領域。我們分析了不同預訓練特徵空間在捕捉領域特定變異方面的差異。我們的實證研究表明，擴散模型的特徵在缺乏明確領域標籤的情況下，能夠出色地分離領域並捕捉細微的領域特定信息。在五個數據集上，我們展示了這個非常簡單的框架相比標準基線經驗風險最小化（ERM），在未見過領域的泛化能力上提升了最多超過4%的測試準確率。關鍵的是，我們的方法在訓練期間訪問領域標籤的大多數算法中表現更優。

English

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.