  Differentiable architecture search (DAS) is a widely researched tool for the
discovery of novel architectures, due to its promising results for image
classification. The main benefit of DAS is the effectiveness achieved through
the weight-sharing one-shot paradigm, which allows efficient architecture
search. In this work, we investigate DAS in a systematic case study of inverse
problems, which allows us to analyze these potential benefits in a controlled
manner. We demonstrate that the success of DAS can be extended from image
classification to signal reconstruction, in principle. However, our experiments
also expose three fundamental difficulties in the evaluation of DAS-based
methods in inverse problems: First, the results show a large variance in all
test cases. Second, the final performance is strongly dependent on the
hyperparameters of the optimizer. And third, the performance of the
weight-sharing architecture used during training does not reflect the final
performance of the found architecture well. While the results on image
reconstruction confirm the potential of the DAS paradigm, they challenge the
common understanding of DAS as a one-shot method.
