Harsh Rangwani, Arihant Jain*, Sumukh K Aithal*, R. Venkatesh Babu
Video Analytics Lab, Indian Institute of Science, Bengaluru
[Project Webpage] [Paper]
Obtain performance close to supervised learning using small amount of labelled data (~10%) in target domain, for adapting a model from source to target domain.
Run the following commands to setup your environment
git clone https://github.com/val-iisc/s3vaada.git
cd s3vaada/
pip install -r requirements.txt
cd models/
wget https://download.pytorch.org/models/resnet50-19c8e357.pth
mv resnet50-19c8e357.pth resnet50.pth
cd ../
Run the following commands to download and preprocess Office-31 dataset
cd datasets/
sh get_office.sh
python preprocess_office31.py
cd ../
Run the following commands to download and preprocess Office-31 dataset
cd datasets/
sh get_office-home.sh
python preprocess_office-home.py
cd ../
python main.py --name w2a-s3vaada --source webcam --target amazon
If you find our work useful cite our paper using the following BibTeX entry.
@InProceedings{Rangwani_2021_ICCV,
author = {Rangwani, Harsh and Jain, Arihant and Aithal, Sumukh K and Babu, R. Venkatesh},
title = {S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {7516-7525}
}