Density estimation plays a crucial role in many data analysis tasks . It is used in tasks as diverse as analyzing population data, spatiallocations 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 . We do not require access to the original PDF during estimation, nor in parametric form, nor as priors, or in the form of many samples . This is enabled by training an unstructuredconvolutional neural network on an infinite stream of synthetic PDFs . We hope thatour publicly available DDE method will be beneficial in many areas of data

Author(s) : Patrik Puchert, Pedro Hermosilla, Tobias Ritschel, Timo Ropinski

Links : PDF - Abstract

Code :
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Keywords : data - estimation - density - tasks - dde -

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