MISA: Mutual Information-Driven Separator With Spectral Alignment

Abstract

With the continual advancement of computer vision techniques, the efficacy of deep learning models has markedly improved. Nonetheless, these accomplishments depend on the premise that both training and test data adhere to the independent and identically distributed (i.i.d.) property. This assumption is frequently violated in practice due to the variability of unobserved data distributions, resulting in performance deterioration. These variations are named domains, and this has motivated the study of robustness to domain shifts, commonly referred to as domain generalization (DG). In this context, DG methodologies have emerged as a novel research paradigm. This research introduces a domain generalization framework, MISA (Mutual Information-driven Separator with Spectral Alignment), which disentangles and learns semantic features via mutual information optimization and spectral alignment. MISA incorporates an attention mechanism and a domain classifier to extract domain information, while three complementary loss functions directly promote feature disentanglement. Additionally, we conceptually establish that the proposed components reduce generalization error in unseen domains and empirically verify the efficacy of MISA by comprehensive comparisons with existing methodologies.