FIXED: Frustratingly Easy Domain Generalization with Mixup (2211.05228v2)

Abstract: Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augment training data to benefit generalization through methods such as Mixup~\cite{zhang2018mixup}. While the vanilla Mixup can be directly applied, theoretical and empirical investigations uncover several shortcomings that limit its performance. Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations. Secondly, Mixup may introduce synthetic noisy data points via random interpolation, which lowers its discrimination capability. Based on the analysis, we propose a simple yet effective enhancement for Mixup-based DG, namely domain-invariant Feature mIXup (FIX). It learns domain-invariant representations for Mixup. To further enhance discrimination, we leverage existing techniques to enlarge margins among classes to further propose the domain-invariant Feature MIXup with Enhanced Discrimination (FIXED) approach. We present theoretical insights about guarantees on its effectiveness. Extensive experiments on seven public datasets across two modalities including image classification (Digits-DG, PACS, Office-Home) and time series (DSADS, PAMAP2, UCI-HAR, and USC-HAD) demonstrate that our approach significantly outperforms nine state-of-the-art related methods, beating the best performing baseline by 6.5\% on average in terms of test accuracy. Code is available at: https://github.com/jindongwang/transferlearning/tree/master/code/deep/fixed.

• The paper’s main contribution is FIXED, which applies Mixup on domain-invariant features to enhance generalization across unseen domains.
• It integrates a large margin loss to clearly separate classes and mitigate noisy feature interpolation, yielding an average 6.5% accuracy improvement.
• The work provides theoretical insights and robust empirical validation on seven benchmarks, outperforming nine state-of-the-art domain generalization methods.

An Analysis of "FIXED: Frustratingly Easy Domain Generalization with Mixup"

The paper "FIXED: Frustratingly Easy Domain Generalization with Mixup" addresses a critical challenge in machine learning known as Domain Generalization (DG). The core objective of DG is to develop models that perform robustly on unseen domains by learning from multiple training domains. This research specifically enhances the Mixup technique to improve the generalization capabilities of models across different domains.

Key Contributions and Methodology

The primary contribution of the paper is the introduction of the domain-invariant Feature MIXup with Enhanced Discrimination (FIXED). This approach builds on the existing Mixup method to overcome identified limitations:

1. Domain-Invariant Feature Mixup (FIX): Traditional Mixup methods struggle to separate domain-specific information from class information, leading to the potential entanglement of features that may harm performance. FIX addresses this by performing Mixup on domain-invariant features instead of raw inputs, allowing the model to focus on relevant classification information without domain interference.
2. Enhanced Discrimination with Large Margin Loss: To mitigate the issue of generating noisy synthetic data points, FIXED incorporates a large margin loss that increases the separation between classes. This addition serves to enhance the discrimination power of the classifiers, ensuring that interpolated features maintain their class-specific characteristics.
3. Theoretical Insights: The paper provides theoretical justification for the superiority of FIXED over traditional Mixup by analyzing distribution coverage and inter-class distances. It demonstrates that FIXED effectively reduces the risk of generating unrecognizable synthetic data, a major concern with the vanilla Mixup method.

Experimental Results

The experimental evaluation conducted on seven benchmark datasets across two modalities—image classification and time series—demonstrates that FIXED outperforms nine state-of-the-art DG methods, achieving an average improvement of 6.5% on test accuracy. This extensive evaluation underlines the practical efficacy of FIXED in handling diverse and challenging domain shifts.

Implications and Future Directions

The introduction of FIXED has both theoretical and practical implications. Theoretically, it emphasizes the importance of disentangling domain and class information when using data augmentation techniques like Mixup. Practically, FIXED offers a straightforward yet effective enhancement to the standard data augmentation pipeline, making it applicable to a broad range of classification tasks.

Future developments could explore the integration of FIXED with other domain-invariant learning frameworks to further boost generalization performance. Additionally, adapting FIXED for application in regression or non-classification tasks might expand its applicability.

Conclusion

The research presented in "FIXED: Frustratingly Easy Domain Generalization with Mixup" contributes significantly to advancements in domain generalization by refining Mixup for better generalization across unseen domains. The proposed enhancements are backed by theoretical insights and robust empirical results, making FIXED a compelling approach for enhancing model generalization in diverse domains.