title: ProDAS: Probabilistic Dataset of Abstract Shapes
creator: Müller, Jens
creator: Ardizzone, Lynton
creator: Köthe, Ullrich
subject: ddc-004
subject: 004 Data processing Computer science
description: 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.
date: 2023-12-08
type: Preprint
type: info:eu-repo/semantics/preprint
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/34135/1/ProDAS.pdf
identifier: DOI:10.11588/heidok.00034135
identifier: urn:nbn:de:bsz:16-heidok-341358
identifier:   Müller, Jens ; Ardizzone, Lynton ; Köthe, Ullrich  (2023) ProDAS: Probabilistic Dataset of Abstract Shapes.  [Preprint]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/34135/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng