Deep models lack robustness to simple input transformations such as rotation,
scaling, and translation, unless they feature a particular invariant
architecture or undergo specific training, e.g., learning the desired
robustness from data augmentations. Alternatively, input transformations can be
treated as a domain shift problem, and solved by post-deployment model
adaptation. Although a large number of methods deal with transformed inputs,
the fundamental relation between input transformations and optimal model
weights is unknown. In this paper, we put forward the configuration subspace
hypothesis that model weights optimal for parameterized continuous
transformations can reside in low-dimensional linear subspaces. We introduce
subspace-configurable networks to learn these subspaces and observe their
structure and surprisingly low dimensionality on all tested transformations,
datasets and architectures from computer vision and audio signal processing
domains. Our findings enable efficient model reconfiguration, especially when
limited storage and computing resources are at stake.

通过子空间可配置网络探讨模型权重最适合为参数化的连续转换低维线性子空间的配置假设，为解决输入转换和最优模型权重之间的基本关系提供了一种新的方法。