TTT++

This is an official implementation for the paper

TTT++: When Does Self-supervised Test-time Training Fail or Thrive? @ NeurIPS 2021
Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi

TL;DR: Online Feature Alignment + Strong Self-supervised Learner 🡲 Robust Test-time Adaptation

• Results
  • reveal limitations and promise of TTT, with evidence through synthetic simulations
  • our proposed TTT++ yields state-of-the-art results on visual robustness benchmarks
• Takeaways
  • both task-specific (e.g. related SSL) and model-specific (e.g. feature moments) info are crucial
  • need to rethink what (and how) to store, in addition to model parameters, for robust deployment

Synthetic

Please check out the code in the synthetic folder.

CIFAR10/100

Please check out the code in the cifar folder.

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{liu2021ttt++,
  title={TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?},
  author={Liu, Yuejiang and Kothari, Parth and van Delft, Bastien Germain and Bellot-Gurlet, Baptiste and Mordan, Taylor and Alahi, Alexandre},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}

Contact

yuejiang [dot] liu [at] epfl [dot] ch