Deep Frequency Filtering for Domain Generalization
This addresses the challenge of making deep learning models more robust across different domains, which is crucial for practical applications, and it presents a novel approach with incremental improvements.
The paper tackles the problem of improving domain generalization in deep neural networks by proposing Deep Frequency Filtering (DFF), which modulates frequency components in latent features to enhance transferable elements, and it shows that applying DFF outperforms state-of-the-art methods on tasks like classification and retrieval.
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of learned features. In this paper, we propose Deep Frequency Filtering (DFF) for learning domain-generalizable features, which is the first endeavour to explicitly modulate the frequency components of different transfer difficulties across domains in the latent space during training. To achieve this, we perform Fast Fourier Transform (FFT) for the feature maps at different layers, then adopt a light-weight module to learn attention masks from the frequency representations after FFT to enhance transferable components while suppressing the components not conducive to generalization. Further, we empirically compare the effectiveness of adopting different types of attention designs for implementing DFF. Extensive experiments demonstrate the effectiveness of our proposed DFF and show that applying our DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.