DARTS for Inverse Problems: A Study on Stability

Abstract

Differentiable architecture search (DARTS) is a widely researched tool for neural architecture search, due to its promising results for image classification. The main benefit of DARTS is the effectiveness achieved through the weight-sharing one-shot paradigm, which allows efficient architecture search. In this work, we investigate DARTS in a systematic case study of inverse problems, which allows us to analyze these potential benefits in a controlled manner. Although we demonstrate that the success of DARTS can be extended from classification to reconstruction, our experiments yield a fundamental difficulty in the evaluation of DARTS-based methods: The results show a large variance in all test cases and the weight-sharing performance of the architecture found during training does not always reflect its final performance. We conclude the necessity to 1) report the results of any DARTS-based methods from several runs along with its underlying performance statistics and 2) show the correlation between the training and final architecture performance.