Frequency Learning

Frequency learning in machine learning focuses on leveraging frequency domain information to improve model performance across diverse tasks, primarily by enhancing feature representation and addressing limitations of spatial-domain-only approaches. Current research emphasizes incorporating learnable frequencies into neural network architectures, such as employing Fourier features or designing specialized modules for frequency-spatial interaction, often within the context of transformers, MLPs, or convolutional neural networks. This approach shows promise in improving accuracy and efficiency for applications ranging from image processing and time series analysis to robotics control and deepfake detection, offering a powerful alternative to traditional methods that rely solely on spatial information.