Official public repo for the ICCV'23 paper "Learning to Transform for Generalizable Instance-wise Invariance"
Project website: https://sutkarsh.github.io/projects/learned_invariance
ArXiv paper: https://arxiv.org/abs/2309.16672
To replicate CIFAR-10 experiments, please run scripts/lila_experiments.sh. The codebase for LILA experiments is largely based off LILA, and we highly recommend citing the original work.
All other experiment scripts can be found in scripts/, and once they have been run, their plots can be created in plots.ipynb.
Alignment code can be found in alignment.ipynb
Checkpoints for experiments/plots:
- For the rotgen experiments, please first download the classifier here
- The width plot in plots.ipynb uses the file augerino_mario_90.npy
To cite our paper, please use the following bibtex
@InProceedings{Singhal_2023_flowinv_ICCV,
author = {Singhal, Utkarsh and Esteves, Carlos and Makadia, Ameesh and Yu, Stella X.},
title = {Learning to Transform for Generalizable Instance-wise Invariance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {6211-6221}
}