Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization
Summary: arXiv:2604.05616v1
Type: Cross
Abstract
Deep learning models for computer vision often suffer from poor generalization when deployed in real-world settings, especially when trained on synthetic data due to the well-known Sim2Real gap. Despite the growing popularity of style transfer as a data augmentation strategy for domain generalization, the literature contains unresolved contradictions regarding three key design axes: the diversity of the style pool, the role of texture complexity, and the choice of style source.
We present a systematic empirical study that isolates and evaluates each of these factors for driving scene understanding, resolving inconsistencies in prior work. Our findings show that:
- Expanding the style pool yields larger gains than repeated augmentation with few styles.
- Texture complexity has no significant effect when the pool is sufficiently large.
- Diverse artistic styles outperform domain-aligned alternatives.
Guided by these insights, we derive StyleMixDG (Style-Mixing for Domain Generalization), a lightweight, model-agnostic augmentation recipe that requires no architectural modifications or additional losses. Evaluated on the GTAV to BDD100k, Cityscapes, and Mapillary Vistas benchmark, StyleMixDG demonstrates consistent improvements over strong baselines, confirming that the empirically identified design principles translate into practical gains. The code will be released on GitHub.
Introduction
The gap between synthetic and real-world data, known as the Sim2Real gap, poses significant challenges for deploying deep learning models in practical applications. Style transfer, a technique that alters the appearance of images while preserving content, has emerged as a potential solution for improving domain generalization. However, ongoing debates about the optimal configuration of style transfer methods have hindered progress in the field.
Key Findings
Our comprehensive analysis has led to several important conclusions regarding the effectiveness of style transfer in domain generalization:
- Diversity of the Style Pool: Increasing the variety of styles used in augmentation significantly enhances model performance, suggesting that a rich style pool is crucial for effective domain adaptation.
- Texture Complexity: Contrary to prior assumptions, we found that texture complexity does not impact performance when a sufficiently diverse style pool is employed.
- Choice of Style Source: Artistic styles that are diverse and varied provide superior results compared to styles closely aligned with the target domain.
Implications and Future Work
The development of StyleMixDG offers a promising direction for future research in domain generalization. By leveraging our findings, practitioners can effectively enhance the robustness of their models without the need for extensive reconfiguration or additional training costs. We anticipate that our work will encourage further exploration into the intersection of style transfer and deep learning for real-world applications.
As we move forward, releasing our code on GitHub will facilitate collaboration and experimentation within the research community, ultimately leading to more refined approaches in tackling the Sim2Real challenge.