- Ubuntu 18.04.1 LTS
- Cuda:
10.2
- Cudnn:
7.6.3
conda
git clone git@github.com:nzw0301/pb-contrastive.git
cd pb-contrastive
# cd pb-contrastive
pyenv install miniconda3-latest
pyenv local miniconda3-latest
conda create --name pac-bayes --file conde/requirements.txt -y
pyenv local miniconda3-latest/envs/pac-bayes
See also the latest PyTorch
.
Note that PyTorch
was 1.2.0
.
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
git submodule sync
git submodule update --init --recursive
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
python setup.py install
Run codes on CNN-README.md
and MLP-README.md
under code
dir.
Then run create-tables.ipynb
to create tables in the main paper.
Option:
Run codes on non-iid-README.md
, then run create-tables-in-Appendix.ipynb
to create tables in the appendix.
Optional: Install parts of experimental dependencies on CPU via Dockerfile to run the jupyter notebook
We provide a docker environment to run notebooks on your local machine without GPUs.
For bash/zsh
:
# cd code
docker build . -t pb-contrastive:latest
docker run -i -p 8888:8888 -v $(pwd):/pb-contrastive/code -w="/pb-contrastive/code" -t pb-contrastive /bin/bash
jupyter notebook --ip=0.0.0.0 --allow-root
For fish
:
# cd code
docker build . -t pb-contrastive:latest
docker run -i -p 8888:8888 -v (pwd):/pb-contrastive/code -w="/pb-contrastive/code" -t pb-contrastive /bin/bash
jupyter notebook --ip=0.0.0.0 --allow-root
@inproceedings{NGG2020,
title = {PAC-Bayesian Contrastive Unsupervised Representation Learning},
author = {Kento Nozawa, Pascal Germain, Benjamin Guedj},
year = {2020},
booktitle = {UAI},
pages = {21--30}
}