Frequency Domain Learning

Frequency domain learning leverages the frequency representation of data (e.g., using Fourier transforms) to improve the efficiency and performance of deep learning models across various applications. Current research focuses on integrating frequency-domain processing with existing spatial-domain architectures, such as U-Nets and Transformers, to enhance feature extraction and reduce computational costs in tasks like image reconstruction, deepfake detection, and 3D segmentation. This approach shows promise in improving model generalizability, reducing parameter counts, and achieving state-of-the-art results in several domains, highlighting its significance for both advancing fundamental understanding and enabling practical applications.