PANOM: Automatic Hyper-parameter Tuning for Inverse Problems

Tianci Liu, Quan Zhang, Qi Lei

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.