@inproceedings{
klee2023a,
title={A Comparison of Equivariant Vision Models with ImageNet Pre-training},
author={David Klee and Jung Yeon Park and Robert Platt and Robin Walters},
booktitle={NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations},
year={2023},
url={https://openreview.net/forum?id=3ItzNHPov9}
}
Neural networks pre-trained on large datasets provide 
useful embeddings for downstream tasks and allow researchers to iterate 
with less compute. For computer vision tasks, ImageNet pre-trained models 
can be easily downloaded for fine-tuning.
However, no such pre-trained models are available that are equivariant to image 
transformations.  In this work, we implement several equivariant versions
of the residual network architecture and publicly release the weights after
training on ImageNet.  Additionally, we perform a comparison of enforced vs.
learned equivariance in the largest data regime to date.
A Comparison of Equivariant Vision Models with ImageNet Pre-training
David Klee Jung Yeon Park Robert Platt Robin Walters