PANOM: Automatic Hyper-parameter Tuning for Inverse ProblemsDownload PDF

19 Oct 2021, 05:51 (edited 26 Nov 2021)NeurIPS 2021 Deep Inverse Workshop PosterReaders: Everyone
  • Abstract: Automated hyper-parameter tuning for unsupervised learning, including inverse problems, remains a long-standing open problem due to the lack of validation data. In this work, we design an automatic tuning criterion for inverse problems and formulate it as a bilevel optimization task. We demonstrate the efficiency of our tuning scheme on various inverse problems and different test and out-of-distribution image samples at no expense of performance drops.
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