PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Paper
• 2308.03977 • Published
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Desert | Ammonite | 1 | 315 | Sky | |
Desert | Ammonite | 1 | 315 | Grass | |
Desert | Ammonite | 1 | 315 | Asphalt | |
Desert | Ammonite | 1.3 | 0 | Default | |
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Desert | Ammonite | 1.3 | 0 | Grass | |
Desert | Ammonite | 1.3 | 0 | Asphalt | |
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Desert | Ammonite | 1.3 | 45 | Sky | |
Desert | Ammonite | 1.3 | 45 | Grass | |
Desert | Ammonite | 1.3 | 45 | Asphalt | |
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Desert | Ammonite | 1.3 | 225 | Sky | |
Desert | Ammonite | 1.3 | 225 | Grass | |
Desert | Ammonite | 1.3 | 225 | Asphalt | |
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Desert | Ammonite | 1.3 | 315 | Sky | |
Desert | Ammonite | 1.3 | 315 | Grass | |
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The PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations. It was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural network generalizes on held out variation factors.
The datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.
If you use one of the PUG datasets, please cite:
@misc{bordes2023pug,
title={PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning},
author={Florian Bordes and Shashank Shekhar and Mark Ibrahim and Diane Bouchacourt and Pascal Vincent and Ari S. Morcos},
year={2023},
eprint={2308.03977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}