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We consider the task of solving generic inverse problems, where one wishes todetermine the hidden parameters of a natural system that will give rise to aparticular set of measurements. Recently many new approaches based upon deeplearning have arisen generating impressive results. We conceptualize thesemodels as different schemes for efficiently, but randomly, exploring the spaceof possible inverse solutions. As a result, the accuracy of each approachshould be evaluated as a function of time rather than a single estimatedsolution, as is often done now. Using this metric, we compare severalstate-of-the-art inverse modeling approaches on four benchmark tasks: twoexisting tasks, one simple task for visualization and one new task frommetamaterial design. Finally, inspired by our conception of the inverseproblem, we explore a solution that uses a deep learning model to approximatethe forward model, and then uses backpropagation to search for good inversesolutions. This approach, termed the neural-adjoint, achieves the bestperformance in many scenarios.

Benchmarking deep inverse models over time, and the neural-adjoint method