Müller, Jens ; Ardizzone, Lynton ; Köthe, Ullrich
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Abstract
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.
| Document type: | Preprint |
|---|---|
| Date Deposited: | 13 Dec 2023 12:43 |
| Date: | 8 December 2023 |
| Faculties / Institutes: | The Faculty of Mathematics and Computer Science > Department of Computer Science |
| DDC-classification: | 004 Data processing Computer science |
