We introduce a technique based on the singular vector canonical correlation
analysis (SVCCA) for measuring the generality of neural network layers across a
continuously-parametrized set of tasks. We illustrate this method by studying
generality in neural networks trained to solve parametrized boundary value
problems based on the Poisson partial differential equation. We find that the
first hidden layer is general, and that deeper layers are successively more
specific. Next, we validate our method against an existing technique that
measures layer generality using transfer learning experiments. We find
excellent agreement between the two methods, and note that our method is much
faster, particularly for continuously-parametrized problems. Finally, we
visualize the general representations of the first layers, and interpret them
as generalized coordinates over the input domain.

使用奇异向量规范相关分析（SVCCA）技术，研究神经网络对于指定问题的普适性，并在求解基于泊松偏微分方程的参数化边界值问题的神经网络中，发现第一层神经网络具有普适性，而更深层次的神经网络则更具有针对性。