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Learning to Transform for Generalizable Instance-wise Invariance

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}
}