A Fourier-based Framework for Domain Generalization (2105.11120v1)

Abstract: Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. Domain generalization aims at tackling this problem by learning transferable knowledge from multiple source domains in order to generalize to unseen target domains. This paper introduces a novel Fourier-based perspective for domain generalization. The main assumption is that the Fourier phase information contains high-level semantics and is not easily affected by domain shifts. To force the model to capture phase information, we develop a novel Fourier-based data augmentation strategy called amplitude mix which linearly interpolates between the amplitude spectrums of two images. A dual-formed consistency loss called co-teacher regularization is further introduced between the predictions induced from original and augmented images. Extensive experiments on three benchmarks have demonstrated that the proposed method is able to achieve state-of-the-arts performance for domain generalization.

• The paper introduces a Fourier-based approach that uses amplitude mix to perturb low-level features while preserving phase information for domain generalization.
• It employs co-teacher regularization with a consistency loss to enforce stability in predictions between original and augmented images.
• Empirical evaluations on DG benchmarks like Digits-DG, PACS, and OfficeHome validate its superior performance compared to state-of-the-art methods.

A Fourier-Based Framework for Domain Generalization: An Expert Overview

In the paper titled "A Fourier-based Framework for Domain Generalization," the authors present a novel approach to enhance the generalization ability of deep neural networks (DNNs) across diverse domains. This research addresses the well-observed limitation where DNNs, trained under the assumption of shared distribution between training and testing data, suffer performance degradation when exposed to out-of-distribution data. Unlike domain adaptation methods, which require labeled or unlabeled data from target domains during training, domain generalization (DG) seeks to generalize to unseen domains without prior exposure.

Core Contributions

The authors introduce a Fourier-transform-based method to tackle the domain generalization problem. The main hypothesis driving this work is that phase components within the Fourier transform of an image encapsulate high-level semantic information that is minimally impacted by domain shifts, in contrast to amplitude components which primarily contain low-level statistical information. To leverage this, they propose a two-fold strategy:

1. Fourier-based Data Augmentation: Amplitude Mix (AM): This method involves linearly interpolating between the amplitude spectrums of two images, effectively perturbing low-level details while preserving phase information. This augmentation encourages models to focus on invariant, higher-order structures within the data, reducing overfitting to low-level patterns.
2. Co-Teacher Regularization: Complementing the data augmentation, a consistency loss is introduced between predictions on original and augmented images. This loss, termed co-teacher regularization, employs a momentum-updated teacher model to impose prediction consistency, reinforcing the focus on the stable phase information.

Empirical Validation

The efficacy of the proposed method is demonstrated through extensive evaluations on standard DG benchmarks, including Digits-DG, PACS, and OfficeHome datasets. The results indicate that the Framework achieves superior performance compared to existing state-of-the-art DG methods, thus validating the hypothesis that phase information robustly supports generalization across unseen domains. Particularly noteworthy is the ability of the proposed framework to significantly enhance performance on domains characterized by considerable discrepancy betwixt training and testing distributions.

Practical and Theoretical Implications

The implications of this research are multifaceted. Practically, it presents a computationally inexpensive approach by eschewing complex adversarial or episodic training strategies often employed in DG tasks, thus offering a more efficient alternative. Theoretically, it reinforces the significance of phase information in image data, challenging conventional model training paradigms that emphasize amplitude-derived features.

Moreover, this work opens avenues for future exploration into other forms of invariant feature extraction and domain-agnostic learning strategies using spectral methods. It prompts further paper into combining Fourier-based techniques with other advanced paradigms, such as meta-learning and self-supervised learning, potentially advancing the robustness of AI systems in generalization tasks.

Future Directions

The authors' exploration into Fourier-transform-utilizing strategies offers a new perspective on tackling distributional shifts, meriting subsequent research to refine and expand upon these methods. Future work may explore adaptive approaches to determine the optimal degree of amplitude perturbation or investigate the integration of this framework with other emerging neural architectures to enhance its generalizability and application scope further.

In summary, this paper contributes a valuable approach to the ongoing efforts in overcoming domain generalization challenges, providing significant insights and tools for creating more broadly applicable and resilient AI models.