Spectral Bias

Spectral bias in neural networks refers to the tendency of these models, during training, to prioritize learning low-frequency components of a function before higher-frequency ones. Current research focuses on understanding this bias across various architectures, including multi-layer perceptrons, convolutional networks, and transformers, and exploring methods to mitigate it, such as novel initialization schemes, normalization techniques, and modified activation functions. Addressing spectral bias is crucial for improving the accuracy and efficiency of neural networks in applications requiring high-frequency detail, such as image processing, scientific computing, and solving partial differential equations.