Frequency Function

Frequency function research in deep learning investigates how neural networks learn functions with varying frequency components, aiming to understand and potentially mitigate inherent biases towards low-frequency information. Current research focuses on analyzing the spectral bias of various architectures, including state-space models, multi-scale deep neural networks, and transformers, and developing techniques to tune or overcome this bias, such as modifying initialization or employing frequency-adaptive mechanisms. This work is significant because understanding and controlling frequency bias is crucial for improving model performance on tasks requiring accurate representation of high-frequency details, such as image denoising and channel estimation in communication systems.