COEP: Cascade Optimization for Inverse Problems with Entropy-Preserving Hyperparameter Tuning

• URL: http://arxiv.org/abs/2210.13983v2
• Date: Wed, 26 Oct 2022 14:13:38 GMT
• Title: COEP: Cascade Optimization for Inverse Problems with Entropy-Preserving Hyperparameter Tuning
• Authors: Tianci Liu, Tong Yang, Quan Zhang, Qi Lei
• Abstract summary: COEP is an automated and principled framework to solve inverse problems with deep generative models. We empirically validate the strength of COEP on denoising and noisy compressed sensing.
• Score: 26.531298115933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose COEP, an automated and principled framework to solve inverse problems with deep generative models. COEP consists of two components, a cascade algorithm for optimization and an entropy-preserving criterion for hyperparameter tuning. Through COEP, the two components build up an efficient and end-to-end solver for inverse problems that require no human evaluation. We establish theoretical guarantees for the proposed methods. We also empirically validate the strength of COEP on denoising and noisy compressed sensing, which are two fundamental tasks in inverse problems.

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