TY  - GEN
TI  - ProDAS: Probabilistic Dataset of Abstract Shapes
A1  - Müller, Jens
A1  - Ardizzone, Lynton
A1  - Köthe, Ullrich
N2  - 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.
Y1  - 2023/12/08/
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/34135/
AV  - public
ID  - heidok34135
ER  -