  In this work we provide an analysis of the distribution of the
post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods.
Previous work has noticed how, for the case of image-classification, this
adaptation only takes place on the last layers of the network. We propose the
more general notion that parameters are updated over a low-dimensional
\emph{subspace} of the same dimensionality as the task-space and show that this
holds for regression as well. Furthermore, the induced subspace structure
provides a method to estimate the intrinsic dimension of the space of tasks of
common few-shot learning datasets.
