ContrastiveMixup Pytorch
This repository contains the code for (arxiv link)
@misc{darabi2021contrastive,
title={Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain},
author={Sajad Darabi and Shayan Fazeli and Ali Pazoki and Sriram Sankararaman and Majid Sarrafzadeh},
year={2021},
eprint={2108.12296},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Dependencies
The project was run on a conda virtual environment on Ubuntu 18.04.5 LTS.
Checkout the requirements.txt file, if you have conda pre-installed cd into the directory where you have downloaded the source code and run the following
conda create -n contrastivemixup python==3.7
conda activate contrastivemixup
pip install -r requirements.txt
Running Experiments
To run experiments they are launched from the train.py file. For example, to run ContrastiveMixup on MNIST use the following command
python train.py -c ./configs/mnist/contrastivemixup.json --pretrain
The trainer, data loader, model, optimizer, settings are all specified in the ./configs/mnist/contrastivemixup.json file.
The --pretrain options specifies whether to run the pretraining phase (i.e. training the encoder).
TODO
- Add instructions to run code
- File structure
- Config file instructions
- Comment/clean code
- Jupyter lab demo