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Code for Curriculum based Dropout Discriminator for Domain Adaptation(CD3A), BMVC, 2019

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Curriculum based Dropout Discriminator for Domain Adaptation(CD3A)

Torch code for Domain Adaptation model(CD3A) . For more information, please refer the paper

Accepted at [BMVC 2019]

Abstract

Domain adaptation is essential to enable wide usage of deep learning based networks trained using large labeled datasets. Adversarial learning based techniques have shown their utility towards solving this problem using a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate, it would be useful if a distribution based discriminator could be used to bridge this gap. This could be achieved using multiple classifiers or using traditional ensemble methods. In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator could suffice to obtain the distribution based discriminator. Specifically, we propose a curriculum based dropout discriminator that gradually increases the variance of the sample based distribution and the corresponding reverse gradients are used to align the source and target feature representations. The detailed results and thorough ablation analysis show that our model outperforms state-of-the-art results.

Result

Requirements

This code is written in Lua and requires Torch.

You also need to install the following package in order to sucessfully run the code.

Download Dataset

Prepare Datasets
  • Download the dataset

Training Steps

We have prepared everything for you ;)

####Clone the repositotry

git clone https://github.com/DelTA-Lab-IITK/CD3A

Dataset prepare

  • Downalod dataset

  • put all source images inside mydataset/train/ such that folder name is class name

  mkdir -p /path_to_wherever_you_want/mydataset/train/ 
  • put all target images inside mydataset/val/ such that folder name is class name
mkdir -p /path_to_wherever_you_want/mydataset/val/ 
  • creare softlink of dataset
 cd CD3A/
 ln -sf /path_to_wherever_you_want/mydataset dataset

Pretrained Alexnet model

  • Download Alexnet pretraine caffe model Link
cd CD3A/  
ln -sf /path_to_where_model_is_downloaded/ pretrained_network 

Train model

cd CD3A/  
./train.sh 

Reference

If you use this code as part of any published research, please acknowledge the following paper

@article{kurmi2019curriculum,
  title={Curriculum based Dropout Discriminator for Domain Adaptation},
  author={Kurmi, Vinod Kumar and Bajaj, Vipul and Subramanian, Venkatesh K and Namboodiri, Vinay P},
  journal={arXiv preprint arXiv:1907.10628},
  year={2019}
}

Contributors