Adaptive sampling using variational autoencoder and reinforcement learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915651038216192 |
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| author | Rasheed, Adil Shahly, Mikael Aleksander Jansen Aftab, Muhammad Faisal |
| author_facet | Rasheed, Adil Shahly, Mikael Aleksander Jansen Aftab, Muhammad Faisal |
| contents | Compressed sensing enables sparse sampling but relies on generic bases and random measurements, limiting efficiency and reconstruction quality. Optimal sensor placement uses historcal data to design tailored sampling patterns, yet its fixed, linear bases cannot adapt to nonlinear or sample-specific variations. Generative model-based compressed sensing improves reconstruction using deep generative priors but still employs suboptimal random sampling. We propose an adaptive sparse sensing framework that couples a variational autoencoder prior with reinforcement learning to select measurements sequentially. Experiments show that this approach outperforms CS, OSP, and Generative model-based reconstruction from sparse measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive sampling using variational autoencoder and reinforcement learning Rasheed, Adil Shahly, Mikael Aleksander Jansen Aftab, Muhammad Faisal Machine Learning Compressed sensing enables sparse sampling but relies on generic bases and random measurements, limiting efficiency and reconstruction quality. Optimal sensor placement uses historcal data to design tailored sampling patterns, yet its fixed, linear bases cannot adapt to nonlinear or sample-specific variations. Generative model-based compressed sensing improves reconstruction using deep generative priors but still employs suboptimal random sampling. We propose an adaptive sparse sensing framework that couples a variational autoencoder prior with reinforcement learning to select measurements sequentially. Experiments show that this approach outperforms CS, OSP, and Generative model-based reconstruction from sparse measurements. |
| title | Adaptive sampling using variational autoencoder and reinforcement learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.03525 |