Skip to content

Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NIPS 2018

License

Notifications You must be signed in to change notification settings

qinenergy/generalize-unseen-domains

 
 

Repository files navigation

Overview

Files

model.py: to build tf's graph

trainOps.py: to train/test

exp_configuration: config file with the hyperparameters

Prerequisites

Python 2.7, Tensorflow 1.6.0

How it works

To obtain MNIST and SVHN dataset, run

mkdir data
python download_and_process_mnist.py
sh download_svhn.sh

To train the model, run

sh run_exp.sh GPU_IDX

where GPU_IDX is the index of the GPU to be used.

About

Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NIPS 2018

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 98.1%
  • Shell 1.9%