Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866915172777459712 |
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| author | Thoreau, Romain Risser, Laurent Achard, Véronique Berthelot, Béatrice Briottet, Xavier |
| author_facet | Thoreau, Romain Risser, Laurent Achard, Véronique Berthelot, Béatrice Briottet, Xavier |
| contents | The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce p$^3$VAE, a variational autoencoder that integrates prior physical knowledge about the latent factors of variation that are related to the data acquisition conditions. p$^3$VAE combines standard neural network layers with non-trainable physics layers in order to partially ground the latent space to physical variables. We introduce a semi-supervised learning algorithm that strikes a balance between the machine learning part and the physics part. Experiments on simulated and real data sets demonstrate the benefits of our framework against competing physics-informed and conventional machine learning models, in terms of extrapolation capabilities and interpretability. In particular, we show that p$^3$VAE naturally has interesting disentanglement capabilities. Our code and data have been made publicly available at https://github.com/Romain3Ch216/p3VAE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_10418 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation Thoreau, Romain Risser, Laurent Achard, Véronique Berthelot, Béatrice Briottet, Xavier Computer Vision and Pattern Recognition Machine Learning 68T45 I.2.6; I.2.10 The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce p$^3$VAE, a variational autoencoder that integrates prior physical knowledge about the latent factors of variation that are related to the data acquisition conditions. p$^3$VAE combines standard neural network layers with non-trainable physics layers in order to partially ground the latent space to physical variables. We introduce a semi-supervised learning algorithm that strikes a balance between the machine learning part and the physics part. Experiments on simulated and real data sets demonstrate the benefits of our framework against competing physics-informed and conventional machine learning models, in terms of extrapolation capabilities and interpretability. In particular, we show that p$^3$VAE naturally has interesting disentanglement capabilities. Our code and data have been made publicly available at https://github.com/Romain3Ch216/p3VAE. |
| title | Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T45 I.2.6; I.2.10 |
| url | https://arxiv.org/abs/2210.10418 |