论文标题
随着时间的推移，基准深层模型和神经间的方法
Benchmarking deep inverse models over time, and the neural-adjoint method
论文作者
论文摘要
我们考虑解决通用逆问题的任务，在这些任务中，人们希望确定自然系统的隐藏参数，这将产生一组特定的测量值。最近，基于深度学习的许多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，以有效但随机地探索可能的反解空间。结果，应像现在通常这样做一样，将每种方法的精度评估为时间的函数，而不是单个估计的解决方案。使用此指标，我们比较了四个基准任务上的几种最新的反向建模方法：两个现有任务，一个可视化的简单任务以及一项来自超材料设计的新任务。最后，受到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近似远期模型，然后使用反向传播来搜索良好的逆解决方案。这种方法称为神经偶像，在许多情况下取得了最佳性能。
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.
