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.

翻译：可微分架构搜索（DAS）是一种被广泛研究的用于发现新颖架构的工具，因其在图像分类任务中展现出的有前景的结果而受到关注。DAS的主要优势在于通过权重共享一次性范式的有效性，从而能够实现高效的架构搜索。在本工作中，我们以逆问题为系统案例研究对DAS进行探究，这使我们能够在受控条件下分析这些潜在优势。我们证明，原则上，DAS的成功可以从图像分类扩展到信号重建。然而，我们的实验也揭示了在逆问题中评估基于DAS的方法时存在的三个根本性困难：第一，所有测试案例的结果均显示出较大的方差；第二，最终性能强烈依赖于优化器的超参数；第三，训练期间使用的权重共享架构的性能不能很好地反映最终所找到架构的性能。虽然图像重建的结果证实了DAS范式的潜力，但它们挑战了将DAS视为一次性方法的普遍认知。