Data-driven deep density estimation

• Published in: Neural computing & applications 2021-12, Vol.33 (23), p.16773-16807
• Main Authors: Puchert, Patrik, Hermosilla, Pedro, Ritschel, Tobias, Ropinski, Timo
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Continuity (mathematics); Data analysis; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; Probability density functions; Spatial data; Training
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-021-06281-3

Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.