Deep Data Density Estimation through Donsker-Varadhan Representation

• URL: http://arxiv.org/abs/2104.06612v1
• Date: Wed, 14 Apr 2021 03:38:32 GMT
• Title: Deep Data Density Estimation through Donsker-Varadhan Representation
• Authors: Seonho Park, Panos M. Pardalos
• Abstract summary: We present a simple yet effective method for estimating the data density using a deep neural network and the Donsker-Varadhan variational lower bound on the KL divergence. We show that the optimal critic function associated with the Donsker-Varadhan representation on the divergence between the data and the uniform distribution can estimate the data density.
• Score: 5.276937617129594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating the data density is one of the challenging problems in deep learning. In this paper, we present a simple yet effective method for estimating the data density using a deep neural network and the Donsker-Varadhan variational lower bound on the KL divergence. We show that the optimal critic function associated with the Donsker-Varadhan representation on the KL divergence between the data and the uniform distribution can estimate the data density. We also present the deep neural network-based modeling and its stochastic learning. The experimental results and possible applications of the proposed method demonstrate that it is competitive with the previous methods and has a lot of possibilities in applied to various applications.

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