Style Factorization: Explore Diverse Style Variation for Domain Generalization

Deep neural networks (DNNs) exhibit noticeable performance degradation when exposed to out-of-distribution samples during testing. This degradation occurs due to the fact that the statistical characteristics (mean and standard deviation) of the features reflect the domain-specific properties of the training data, causing DNNs to be biased towards unseen distributions. One prominent approach to mitigate this bias is training networks with style augmented data, which helps alleviate the reliance on specific style distributions. However, existing methods either have limitations in terms of the number of styles they can handle or rely on intricate pipelines. In this paper, we propose a novel and simple approach called Style Factorization (StylF) to generate novel styles by identifying the directions that exhibit the most significant variations in style. We formulate this problem as a constrained optimization task and decompose the feature statistics matrix of the training data to generate meaningful and diverse novel styles. Through extensive experiments conducted on three publicly available domain generalization benchmarks (PACS, OfficeHome, DomainNet), we demonstrate that our proposed method achieves SOTA performance.