Early Stopping for Deep Image Prior

Understand

Deep image prior (DIP) and its variants have showed remarkable potential for solving inverse problems in computer vision, without any extra training data.

• Practical DIP models are often substantially overparameterized.
• During the fitting process, these models learn mostly the desired visual content first, and then pick up the potential modeling and observational noise, i.e., overfitting.
• Thus, the practicality of DIP often depends critically on good early stopping (ES) that captures the transition period.