Fourier Basis Density Model
Alfredo De la Fuente Saurabh Singh Johannes Ballé
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.