ProGAN

a GAN network, from paper Network Embedding via Proximity Generative

From his vedio and project.
https://www.youtube.com/c/AladdinPersson
https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/GANs/ProGAN

link

Results

animefaces256cleaner dataset

The dataset can be downloaded from Kaggle: link.

Training

Edit the config.py file to match the setup you want to use. Then run train.py

My Model

link：https://pan.baidu.com/s/1-weFTGA-wyhaPQRvyot6kw 
password：rjm3

Testing

Edit the config.py file and run it,you can see the results in saved_256.You can run the watch_data.py to see all the results you want to see.

ProGAN paper

Progressive Growing of GANs for Improved Quality, Stability, and Variation by Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen

Abstract

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

@misc{karras2018progressive,
      title={Progressive Growing of GANs for Improved Quality, Stability, and Variation}, 
      author={Tero Karras and Timo Aila and Samuli Laine and Jaakko Lehtinen},
      year={2018},
      eprint={1710.10196},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}