A Closer Look at Few-shot Image Generation

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022

[Paper] [Project Page] [Slides] [Poster] [Data Repository]

Code/Project page will be actively updated.

TL, DR

In this work, we take a closer look at few-shot image generation (FSIG) problem. Our analysis discovers that: current methods for FSIG achieve similar quality of generated images after few-shot adaptation on target training images, while the main difference is on the diversity of those generated samples. State-of-the-art FSIG algorithms generates visual-pleasant images with both high-quality and high-diversity.

Based on our analysis, we propose a mutual-information based contrastive learning algorithm (DCL) that preserves the diversity of generated images during adaptation.

Installation and Environment:

Platform: Linux
Tesla V100 GPUs / (or A100 GPUs)
PyTorch 1.7.0
Python 3.6.9
lmdb, tqdm

Alternatively, A suitable conda environment named fsig can be created and activated with:

git clone https://github.com/yunqing-me/A-Closer-Look-at-FSIG.git
conda env create -f environment.yml
conda activate fsig
cd A-Closer-Look-at-FSIG

Prepare datasets

Step 1.

Prepare the few-shot training dataset using lmdb format

For example, download the 10-shot target set, Babies (Link) and AFHQ-Cat(Link), and organize your directory as follows:

10-shot-{babies/afhq_cat}
└── images		
    └── image-1.png
    └── image-2.png
    └── ...
    └── image-10.png

Then, transform to lmdb format:

python prepare_data.py --input_path [your_data_path_of_{babies/afhq_cat}] --output_path ./_processed_train/[your_lmdb_data_path_of_{babies/afhq_cat}]

Step 2.

Prepare the entire target dataset for evaluation

For example, download the entire dataset, Babies(Link) and AFHQ-Cat(Link), and organize your directory as follows:

entire-{babies/afhq_cat}
└── images		
    └── image-1.png
    └── image-2.png
    └── ...
    └── image-n.png

Then, transform to lmdb format for evaluation

python prepare_data.py --input_path [your_data_path_of_entire_{babies/afhq_cat}] --output_path ./_processed_test/[your_lmdb_data_path_of_entire_{babies/afhq_cat}]

Step 3.

Download the GAN model pretrained on FFHQ from here. Then, save it to ./_pretrained/style_gan_source_ffhq.pt.

Bibtex

If you find our work useful in your research, please consider citing our paper:

@InProceedings{Zhao_2022_CVPR,
    author    = {Zhao, Yunqing and Ding, Henghui and Huang, Houjing and Cheung, Ngai-Man},
    title     = {A Closer Look at Few-Shot Image Generation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {9140-9150}
}