%0 Generic
%A Müller, Jens
%A Ardizzone, Lynton
%A Köthe, Ullrich
%D 2023
%F heidok:34135
%R 10.11588/heidok.00034135
%T ProDAS: Probabilistic Dataset of Abstract Shapes
%U https://archiv.ub.uni-heidelberg.de/volltextserver/34135/
%X We introduce a novel and comprehensive dataset, named ProDAS, which enables the generation of diverse objects with varying shape, size, rotation, and texture/color through a latent factor model. ProDAS offers complete access and control over the data generation process, serving as an ideal environment for investigating disentanglement, causal discovery, out-of-distribution detection, and numerous other research questions. We provide pre-defined functions for the important cases of creating distinct and interconnected distributions, allowing the investigation of distribution shifts and other intriguing applications. The library can be found at https://github.com/XarwinM/ProDAS.