Style Normalization and Restitution for Domain Generalization and Adaptation (2101.00588v3)

Abstract: For many practical computer vision applications, the learned models usually have high performance on the datasets used for training but suffer from significant performance degradation when deployed in new environments, where there are usually style differences between the training images and the testing images. An effective domain generalizable model is expected to be able to learn feature representations that are both generalizable and discriminative. In this paper, we design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure both high generalization and discrimination capability of the networks. In the SNR module, particularly, we filter out the style variations (e.g, illumination, color contrast) by performing Instance Normalization (IN) to obtain style normalized features, where the discrepancy among different samples and domains is reduced. However, such a process is task-ignorant and inevitably removes some task-relevant discriminative information, which could hurt the performance. To remedy this, we propose to distill task-relevant discriminative features from the residual (i.e, the difference between the original feature and the style normalized feature) and add them back to the network to ensure high discrimination. Moreover, for better disentanglement, we enforce a dual causality loss constraint in the restitution step to encourage the better separation of task-relevant and task-irrelevant features. We validate the effectiveness of our SNR on different computer vision tasks, including classification, semantic segmentation, and object detection. Experiments demonstrate that our SNR module is capable of improving the performance of networks for domain generalization (DG) and unsupervised domain adaptation (UDA) on many tasks. Code are available at https://github.com/microsoft/SNR.

• The paper introduces the Style Normalization and Restitution (SNR) module, which uses Instance Normalization to reduce style variations and a restitution process to restore task-relevant features.
• Experimental results on datasets like PACS and Office-Home show that SNR significantly improves performance in both domain generalization and unsupervised domain adaptation tasks.
• The SNR module offers a practical and effective approach to enhance model robustness against domain shifts, advancing feature disentanglement and generalization capabilities.

Style Normalization and Restitution for Domain Generalization and Adaptation

The paper "Style Normalization and Restitution for Domain Generalization and Adaptation" presents a novel framework designed to address domain generalization and unsupervised domain adaptation problems prevalent in computer vision tasks. The authors introduce the Style Normalization and Restitution (SNR) module aimed at enhancing both generalization and discrimination capabilities of deep learning models, particularly in scenarios where domain shifts between training and testing data occur.

Methodology

The primary innovation of the paper lies in the design of the SNR module which incorporates two key phases:

1. Style Normalization: This phase utilizes Instance Normalization (IN) to filter out style variations such as illumination and color contrast that often lead to domain gaps between datasets. By reducing style discrepancies among different samples and domains, IN facilitates a more generalized feature representation which is less sensitive to instance-specific styles.
2. Feature Restitution: While IN aids in style normalization, it can inadvertently remove task-relevant discriminative features. To counteract this, the paper proposes a restitution process wherein task-relevant discriminative features are distilled from the residuals—differences between the original features and style-normalized features—and reincorporated into the network. This restitution process is further refined using a dual restitution loss constraint that encourages effective disentanglement and separation of task-relevant and task-irrelevant features.

Results

The paper validates the functionality of the SNR module across multiple computer vision tasks, including object classification, semantic segmentation, and object detection. The experimental results demonstrate notable improvements in domain generalization (DG) and unsupervised domain adaptation (UDA) settings:

• On the PACS and Office-Home datasets, the SNR module significantly outperforms traditional methods, achieving superior average accuracies across various tasks.
• In UDA contexts, integration of SNR with existing methods like M3SDA results in substantial accuracy gains, underscoring the module's ability to bridge domain gaps effectively.

Implications

The introduction of the SNR module offers noteworthy implications for both theoretical and practical domains. Theoretically, it advances the understanding of feature disentanglement and domain adaptation in neural networks, proposing a robust mechanism for handling domain shifts without needing target domain labels. Practically, the simplicity and effectiveness of SNR make it a valuable tool for improving model generalization in real-world applications, where environmental and equipment-related discrepancies are rampant.

Future Directions

The potential of the SNR module opens avenues for further research:

• Integration with other domain adaptation techniques to explore synergetic effects for complex scenarios involving multiple and diverse domain shifts.
• Application to a broader range of AI tasks beyond vision, potentially enhancing the adaptability and robustness of models in fields like natural language processing and speech recognition.
• Exploration of SNR's impact on model interpretability and its role in improving the transparency of AI systems in different domains.

This paper sets a milestone in enhancing model robustness against domain variance, paving the way for more generalizable AI systems capable of performing reliably in diverse conditions. The SNR module exemplifies a step forward in the ongoing quest to mitigate domain-related challenges in machine learning.