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.

翻译：我们提出了一种轻量级、灵活且可端到端训练的基于约束傅立叶基参数化的概率密度模型。通过评估其在逼近一系列通常难以拟合的多模态一维密度分布上的表现，发现与文献[1]中提出的深度因子化模型相比，我们的模型在相近的计算预算下实现了更低的交叉熵。此外，我们还在一个示例压缩任务中评估了该方法，验证了其在学习型压缩中的实用性。