• python: 3.6.8
• CUDA: 11.2
• cudnn: 8005

pip install -r requirements.txt

git clone git@github.com:NVIDIA/apex.git
cd apex
git checkout 54b93919aadc117cbab1fe5a2af4664bb9842928
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Install an optional library for efficient experiments.

# Install gnu-parallel from source.
wget https://ftp.gnu.org/gnu/parallel/parallel-20210622.tar.bz2
tar -jxvf parallel-20210622.tar.bz2
cd parallel-20210622
./configure --prefix=~/bin
make
make install

• Toy: Please run code/jobs/toy/circle.sh.
• Vision: Please run {cifar10,cifar100}/contrastive/seed_*.sh under code/jobs/vision directory.
• Language: Please run code/jobs/language/wiki3029/contrastive.sh.

Note that this repo manages the experimental results by using wandb. Please replace INPUT_YOUR_ENTITY with your wandb username in codes that you would like to run.

Please run ./code/notebooks/extract_eval_weights_from_wandb.ipynb.

• Vision: Please run the {cifar10,cifar100}/mean_eval.sh, {cifar10,cifar100}/linear_eval.sh and {cifar10,cifar100}/contrastive_eval.sh scripts under code/jobs/vision directory.
• Language: Please run the mean_eval.sh, linear_eval.sh and contrastive_eval.sh scripts under code/jobs/language/wiki3029 directory.

Please run the following notebooks to generate plots

• scripts/plot_k_msuploss.py: Figure 3 (a)
• scripts/plot_k_nceloss.py: Figure 3 (b)
• scripts/compare_upper_bound.py: Figure 4
• scripts/plot_toy_trajectory.py: Figure 5
• scripts/make_toy_figure.py: Figure 6

Note that the scripts for Figures 5 and 6 require the results generated by code/jobs/toy/circle.sh.

• code/notebooks/bound.ipynb: Figures 1 and 9
• code/notebooks/ck_heatmap.ipynb Figures 7 and 8

Note:: this codebase tracks experiments using Weights & Biases, but the default wandb might cause hanging at the beginning of training of distributed training code. To avoid this, please set an environment variable as follows:

WANDB_START_METHOD="thread"

@inproceedings{BNN2022,
    title = {{On the Surrogate Gap between Contrastive and Supervised Losses}},
    author = {Bao, Han and Nagano, Yoshihiro and Nozawa, Kento},
    year = {2022},
    booktitle = {ICML},
    pages = {1585--1606},
}

• PMLR
• arXiv
• Poster