  Humans understand a set of canonical geometric transformations (such as
translation and rotation) that support generalization by being untethered to
any specific object. We explore inductive biases that help a neural network
model learn these transformations in pixel space in a way that can generalize
out-of-domain. Specifically, we find that high training set diversity is
sufficient for the extrapolation of translation to unseen shapes and scales,
and that an iterative training scheme achieves significant extrapolation of
rotation in time.
