Awesome

Code release for Test-Time Training with Self-Supervision for Generalization under Distribution Shifts.
This code produces our results on CIFAR-10-C and CIFAR-10.1. The ImageNet results are produced by this repository.

Requirements

1. Our code requires pytorch version 1.0 or higher, with at least one modern GPU of adequate memory.
2. We ran our code with python 3.7. Compatibility with python 2 is possible maybe with some modifications.
3. Most of the packages used should be included with anaconda, except maybe two small utilities:
   • tqdm, which we installed with conda install tqdm.
   • colorama, which we installed with conda install colorama.
4. Download the two datasets into the same folder:
   • CIFAR-10-C (Hendrycks and Dietterich) from this repository, which links to this shared storage.
   • CIFAR-10.1 (Recht et al.) from this repository.

Steps

1. Clone our repository with git clone https://github.com/yueatsprograms/ttt_cifar_release.
2. Inside the repository, set the data folder to where the datasets are stored by editing:
   • --dataroot argument in main.py.
   • --dataroot argument in baseline.py.
   • dataroot variable in script_test_c10.py.
3. Run script.sh for the main results, and script_baseline.sh for the baseline results.
4. The results are stored in the respective folders in results/.
5. Once everything is finished, the results can be compiled and visualized with the following utilities:
   • show_table.py parses the results into tables and prints them.
   • show_plot.py makes bar plots like those in our paper, and prints the tables in latex format; requires first running show_table.py.
   • show_grad.py makes the gradient correlation plot in our paper.