DARTS for Inverse Problems: a Study on StabilityDownload PDF

19 Oct 2021, 05:51 (edited 01 Dec 2021)NeurIPS 2021 Deep Inverse Workshop PosterReaders: Everyone
  • Keywords: Architecture Search, NAS, DARTS, Inverse Problems
  • TL;DR: DARTS evaluation for inverse problems is difficult and weight-sharing performance does not necessarily predict architecture performance.
  • 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.
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