Online Architecture Search for Compressed Sensing based on Hypergradient Descent
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917275728084992 |
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| author | Nakai-Kasai, Ayano Nakane, Yusuke Wadayama, Tadashi |
| author_facet | Nakai-Kasai, Ayano Nakane, Yusuke Wadayama, Tadashi |
| contents | AS-ISTA (Architecture Searched-Iterative Shrinkage Thresholding Algorithm) and AS-FISTA (AS-Fast ISTA) are compressed sensing algorithms introducing structural parameters to ISTA and FISTA to enable architecture search within the iterative process. The structural parameters are determined using deep unfolding, but this approach requires training data and the large overhead of training time. In this paper, we propose HGD-AS-ISTA (Hypergradient Descent-AS-ISTA) and HGD-AS-FISTA that use hypergradient descent, which is an online hyperparameter optimization method, to determine the structural parameters. Experimental results show that the proposed method improves performance of the conventional ISTA/FISTA while avoiding the need for re-training when the environment changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14411 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Online Architecture Search for Compressed Sensing based on Hypergradient Descent Nakai-Kasai, Ayano Nakane, Yusuke Wadayama, Tadashi Signal Processing AS-ISTA (Architecture Searched-Iterative Shrinkage Thresholding Algorithm) and AS-FISTA (AS-Fast ISTA) are compressed sensing algorithms introducing structural parameters to ISTA and FISTA to enable architecture search within the iterative process. The structural parameters are determined using deep unfolding, but this approach requires training data and the large overhead of training time. In this paper, we propose HGD-AS-ISTA (Hypergradient Descent-AS-ISTA) and HGD-AS-FISTA that use hypergradient descent, which is an online hyperparameter optimization method, to determine the structural parameters. Experimental results show that the proposed method improves performance of the conventional ISTA/FISTA while avoiding the need for re-training when the environment changes. |
| title | Online Architecture Search for Compressed Sensing based on Hypergradient Descent |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.14411 |