Keywords: early stopping, deep image prior, deep generative models, overparametrization, overfitting
Abstract: Deep image prior (DIP) and its variants have shown remarkable potential for solving inverse problems in computational imaging (CI), needing no separate training data. Practical DIP models are often substantially overparameterized. During the learning process, these models learn the desired visual content first and then pick up the potential modeling and observational noise, i.e., overfitting. Thus, the practicality of DIP hinges on early stopping (ES) that captures the transition period. In this regard, the majority of prior DIP works for CI tasks only demonstrate the potential of the models---reporting the peak performance against the groundtruth but providing no clue about how to operationally obtain near-peak performance without access to the groundtruth. In this paper, we set to break this practicality barrier of DIP, and propose an efficient ES strategy that consistently detects near-peak performance across several CI tasks and DIP variants. Simply based on the running variance of DIP intermediate reconstructions, our ES method not only outpaces the existing ones---which only work in very narrow regimes, but also remains effective when combined with methods that try to mitigate overfitting.
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