# Learning Canonical Transformations

arXiv: https://arxiv.org/abs/2011.08822 · HTML: https://mlsanity.com/paper/2011.08822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Adaptable Hamiltonian neural networks](https://mlsanity.com/paper/2102.13235/) (2021)

## Abstract (arXiv)

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.