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DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

Pytorch implementation for our DivCo. We propose a simple yet effective regularization term named latent-augmented contrastive loss that can be applied to arbitrary conditional generative adversarial networks in different tasks to alleviate the mode collapse issue and improve the diversity.

Contact: Rui Liu (ruiliu@link.cuhk.edu.hk)

Paper

DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network<br> Rui Liu, Yixiao Ge, Ching Lam Choi, Xiaogang Wang, and Hongsheng Li<br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 <br> [arxiv]

Citing DivCo

If you find DivCo useful in your research, please consider citing:

@inproceedings{Liu_DivCo,
  author = {Liu, Rui and Ge, Yixiao and Choi, Ching Lam and Wang, Xiaogang and Li, Hongsheng},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
  title = {DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network},
  year = {2021}
}

Framework

<img src='imgs/framework.png' width="900px">

Usage

Prerequisites

Install

git clone https://github.com/ruiliu-ai/DivCo.git

Training Examples

Download datasets for each task into the dataset folder

mkdir datasets

Label-conditoned Image Generation

cd DivCo/DivCo-DCGAN
python train.py --dataroot ./datasets/Cifar10

Paried Image-to-image Translation

You can download the facades and maps datasets from the BicycleGAN [Github Project]. <br> We employ the network architecture of the BicycleGAN and follow its training process.

cd DivCo/DivCo-BicycleGAN
python train.py --dataroot ./datasets/facades

Unpaired Image-to-image Translation

You can download the datasets from the DRIT [Github Project]. <br> Specify --concat 0 for Cat2Dog to handle large shape variation translation

cd DivCo/DivCo-DRIT
python train.py --dataroot ./datasets/cat2dog --concat 0 --lambda_contra 0.1
python train.py --dataroot ./datasets/yosemite --concat 1 --lambda_contra 1.0

Pre-trained Models

Download and save them into

./models/

Evaluation

For BicycleGAN, DRIT and MSGAN, please follow the instructions of corresponding github projects of the baseline frameworks for more evaluation details. <br>

Testing Examples

DivCo-DCGAN <br>

python test.py --dataroot ./datasets/Cifar10 --resume ./models/DivCo-DCGAN/00199.pth

DivCo-BicycleGAN <br>

python test.py --dataroot ./datasets/facades --checkpoints_dir ./models/DivCo-BicycleGAN/facades --epoch 400

python test.py --dataroot ./datasets/maps --checkpoints_dir ./models/DivCo-BicycleGAN/maps --epoch 400

DivCo-DRIT <br>

python test.py --dataroot ./datasets/yosemite --resume ./models/DivCo-DRIT/yosemite/01199.pth --concat 1

python test.py --dataroot ./datasets/cat2dog --resume ./models/DivCo-DRIT/cat2dog/01199.pth --concat 0

Reference

Quantitative Evaluation Metrics