Awesome
Official Pytorch implementation of "Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks" (CVPR 2022)
Chanyong Jung*, Gihyun Kwon*, Jong Chul Ye (* co-first author)
Link: https://arxiv.org/abs/2203.01532
Supplementary Material: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jung_Exploring_Patch-Wise_Semantic_CVPR_2022_supplemental.pdf
<p align="center"> <img src="https://user-images.githubusercontent.com/52989204/163761652-cc999aa5-db8f-4e34-be4e-8fa4fa706c2e.png" width="900"/> </p>
Cite
@inproceedings{jung2022exploring,
title={Exploring patch-wise semantic relation for contrastive learning in image-to-image translation tasks},
author={Jung, Chanyong and Kwon, Gihyun and Ye, Jong Chul},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={18260--18269},
year={2022}
}
Environment
$ conda create -n SRC python=3.6
$ pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
$ conda install -c conda-forge packaging
$ conda install -c conda-forge visdom
$ conda install -c conda-forge gputil
$ conda install -c conda-forge dominate