Domain Generalization with Fourier Transform and Soft Thresholding

• URL: http://arxiv.org/abs/2309.09866v3
• Date: Tue, 12 Dec 2023 22:37:06 GMT
• Title: Domain Generalization with Fourier Transform and Soft Thresholding
• Authors: Hongyi Pan, Bin Wang, Zheyuan Zhang, Xin Zhu, Debesh Jha, Ahmet Enis Cetin, Concetto Spampinato, Ulas Bagci
• Abstract summary: Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. To overcome this limitation, we introduce a soft-thresholding function in the Fourier domain. The innovative nature of the soft thresholding fused with Fourier-transform-based domain generalization improves neural network models' performance.
• Score: 10.50210846364862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-transform-based domain generalization methods have gained popularity primarily because they exploit the power of Fourier transformation to capture essential patterns and regularities in the data, making the model more robust to domain shifts. The mainstream Fourier-transform-based domain generalization swaps the Fourier amplitude spectrum while preserving the phase spectrum between the source and the target images. However, it neglects background interference in the amplitude spectrum. To overcome this limitation, we introduce a soft-thresholding function in the Fourier domain. We apply this newly designed algorithm to retinal fundus image segmentation, which is important for diagnosing ocular diseases but the neural network's performance can degrade across different sources due to domain shifts. The proposed technique basically enhances fundus image augmentation by eliminating small values in the Fourier domain and providing better generalization. The innovative nature of the soft thresholding fused with Fourier-transform-based domain generalization improves neural network models' performance by reducing the target images' background interference significantly. Experiments on public data validate our approach's effectiveness over conventional and state-of-the-art methods with superior segmentation metrics.

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