Skip to yearly menu bar Skip to main content


Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Matthew Tancik · Pratul Srinivasan · Ben Mildenhall · Sara Fridovich-Keil · Nithin Raghavan · Utkarsh Singhal · Ravi Ramamoorthi · Jonathan Barron · Ren Ng

Poster Session 5 #1441


We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains. These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs to represent complex 3D objects and scenes. Using tools from the neural tangent kernel (NTK) literature, we show that a standard MLP has impractically slow convergence to high frequency signal components. To overcome this spectral bias, we use a Fourier feature mapping to transform the effective NTK into a stationary kernel with a tunable bandwidth. We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities.

Chat is not available.