tancik/fourier-feature-networks resource

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1370 stars · 141 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Research code accompanying the NeurIPS 2020 paper 'Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains'. It provides a demo notebook and experiment scripts showing how Fourier feature mappings help MLPs learn high-frequency functions.

Use cases

• learn how Fourier feature mappings improve MLP regression
• reproduce paper experiments on high-frequency function learning
• understand spectral bias and the neural tangent kernel
• fit high-frequency signals like images and audio with an MLP
• study techniques behind NeRF-style coordinate networks

When to choose

• you want reference implementations of Fourier feature mappings from the original paper
• you are studying spectral bias in neural networks
• you need a Colab-ready demo of coordinate-based MLPs

When to avoid

• you need a production-ready library with maintained APIs
• you want a general-purpose deep learning framework
• you need active support or new features

Facets

learning-resource · maturity maintenance

machine-learning deep-learning image-processing machine-learning deep-learning computer-vision graphics python fourier-features neural-tangent-kernel mlp research-paper jupyter-notebook spectral-bias neural-representation

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