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arXiv · 2402.15345

Fourier Basis Density Model

Alfredo De la Fuente·Saurabh Singh·Johannes Ballé
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Abstract

We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis. We assess its performance at approximating a range of multi-modal 1D densities, which are generally difficult to fit. In comparison to the deep factorized model introduced in [1], our model achieves a lower cross entropy at a similar computational budget. In addition, we also evaluate our method on a toy compression task, demonstrating its utility in learned compression.

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Alfredo De la Fuente, Saurabh Singh, Johannes Ballé. 2024-02-23. Fourier Basis Density Model. https://arxiv.org/abs/2402.15345

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