Self-supervised Domain Adaptation

Repository for the paper "Self-supervised Domain adaptation for Computer Vision Tasks".

@article{self-supervised-da:2019,
  title={Self-supervised Domain Adaptation for Computer Vision Tasks},
  author={Jiaolong, Xu and Liang, Xiao and Antonio M. López},
  journal={IEEE Access},
  volume={7},
  pages={156694-156706}
  year={2019}
}

Requirements

• python3.5+

• pytorch 1.0+

Prepare dataset

Please find the PACS dataset from this link

The directories of the dataset are as following:

.
├── datasets
│   └── PACS
│       └── kfold
│           ├── art_painting
│           ├── cartoon
│           ├── photo
│           └── sketch

Running experiments

The configuration files for each experiment can be found at config/ folder.

For example:

python3 main.py --config configs/rotate_pacs_photo.yaml

To reproduce the results, running each experiment for three repeatitions with random seeds from 100, 200 and 300.

Results

Method	art paint.	cartoon	sketches	photo	Avg.
SRC[1]	77.85	74.86	67.74	95.73	79.05
JigGen[1]	84.88	81.07	79.05	97.96	85.74
Ours(SRC)	79.33	76.75	64.40	96.39	79.22
Ours(Jigsaw)	84.93	83.85	69.04	93.92	82.94
Ours(Rot)	89.35	84.14	74.49	98.24	86.56

Acknowledgement

Thanks for the open source of JigGen for reference implementation!

References

[1] F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi. Domain generalization by solving jigsaw puzzles. In CVPR, 2019.