随着时间的推移对深度逆模型进行基准测试，以及神经伴随方法
Benchmarking deep inverse models over time, and the neural-adjoint method
摘要：
我们考虑的任务是解决一般的反问题，其中一个希望确定隐藏的参数的自然系统，将产生一组特定的测量。最近出现了许多基于深度学习的新方法，产生了令人印象深刻的结果。我们将这些模型概念化为不同的方案，有效地，但随机地，探索可能的逆解空间。因此，每种方法的准确性应该作为时间的函数来评估，而不是像现在经常做的那样，作为一个单一的估计解决方案。使用这个指标，我们比较了几个国家的最先进的逆建模方法的四个基准任务：两个现有的任务，一个简单的任务可视化和一个新的任务从超材料设计。最后，受我们对逆问题的概念的启发，我们探索了一种解决方案，该解决方案使用深度学习模型来近似正向模型，然后使用反向传播来搜索良好的逆解。这种方法，称为神经伴随，在许多情况下实现了最佳性能。
英文摘要：
We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements. Recently many new approaches based upon deep learning have arisen generating impressive results. We conceptualize these models as different schemes for efficiently, but randomly, exploring the space of possible inverse solutions. As a result, the accuracy of each approach should be evaluated as a function of time rather than a single estimated solution, as is often done now. Using this metric, we compare several state-of-the-art inverse modeling approaches on four benchmark tasks: two existing tasks, one simple task for visualization and one new task from metamaterial design. Finally, inspired by our conception of the inverse problem, we explore a solution that uses a deep learning model to approximate the forward model, and then uses backpropagation to search for good inverse solutions. This approach, termed the neural-adjoint, achieves the best performance in many scenarios.
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