mlsanityData to 7 Oct 2026

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

arXiv 2006.10739 ↗First posted 18 Jun 2020cs.CV cs.LGcited by 2,170

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Cited by 153 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 fails to learn high frequencies both in theory and in practice. 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.

Key facts. Cited by 2,170 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2020.

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Citing papers per year: 2021: 181; 2022: 320; 2023: 406; 2024: 440; 2025: 416; 2026: 407.

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