Pretraining and Consistency

This repository contains the implementation for the BMVC'21 paper "Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency".

Install

pip install -r requirements.txt

Data preparation

Our implementation follows a similar data format to MME. To download and set up DomainNet data for the real and sketch domains, run

sh download_data.sh

For setting up data for clipart and painting, uncomment corresponding lines in download_data.sh. Images go into the following directories:

./data/multi/real/<category_name>,

./data/multi/sketch/<category_name>

And file lists in the txt format are available in the following directories:

./data/txt/multi/labeled_source_images_real.txt,

./data/txt/multi/unlabeled_target_images_sketch_3.txt,

./data/txt/multi/validation_target_images_sketch_3.txt.

Office, Office-Home and VisDA-17 datasets, can be set up in a similar manner, with filelists provided in their respective directories in data/.

Training

For pretraining with rotation prediction

python addnl_scripts/pretrain/rot_pred.py --batch_size=16 --steps=5001 --dataset=multi --source=real --target=sketch --save_dir=expts/rot_pred

For semi-supervised domain adaptation (SSDA) training

python main.py --steps=50001 --dataset=multi --source=real --target=sketch --backbone=expts/rot_pred/checkpoint.pth.tar

Other experiments

The following are commands to run the experiments for the corresponding results in the paper.

• Virtual Adversarial Training (VAT) : Adversarial perturbation using VAT instead of image augmentation can be used as follows:

python main.py --vat_tw=0.01 --steps=10001 --net=alexnet --aug_level=0 --dataset=office_home --source=Real --target=Clipart

• Pretraining with Momentum Contrast (MoCo)

python addnl_scripts/pretrain/moco.py --steps=5001 --dataset=office_home --source=Real --target=Clipart --save_dir=expts/moco_pretraining

• Evaluation scripts : To compute the -distance, use (while providing the correct dataset, source, target and net)

python addnl_scripts/eval/compute_proxy_distance.py --backbone_path=path/to/backbone

For computing nearest neighbors classifier accuracy, use

python addnl_scripts/eval/nn_classifier.py --backbone_path=path/to/backbone

Citation

If you find this repository useful for your work, please consider citing:

@article{mishra2021surprisingly,
  title={Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency},
  author={Mishra, Samarth and Saenko, Kate and Saligrama, Venkatesh},
  journal={arXiv preprint arXiv:2101.12727},
  year={2021}
}