Roy Ganz • Michael Elad
This repository contains code for the paper "BIGRoC: Boosting Image Generation via a Robust Classifier"
BIGRoC: Boosting Image Generation via a Robust Classifier
Roy Ganz, Michael EladAbstract: The interest of the machine learning community in image synthesis has grown significantly in recent years, with the introduction of a wide range of deep generative models and means for training them. In this work, we propose a general model-agnostic technique for improving the image quality and the distribution fidelity of generated images, obtained by any generative model. Our method, termed BIGRoC (Boosting Image Generation via a Robust Classifier), is based on a post-processing procedure via the guidance of a given robust classifier and without a need for additional training of the generative model. Given a synthesized image, we propose to update it through projected gradient steps over the robust classifier, in an attempt to refine its recognition. We demonstrate this post-processing algorithm on various image synthesis methods and show a significant improvement of the generated images, both quantitatively and qualitatively, on CIFAR-10 and ImageNet. Specifically, BIGRoC improves the image synthesis state of the art on ImageNet 128x128 by 14.81%, attaining an FID score of 2.53 and on 256x256 by 7.87%, achieving an FID of 3.63.
Ganz, Roy, and Michael Elad. "BIGRoC: Boosting Image Generation via a Robust Classifier." Transactions on Machine Learning Research (2023).
@article{
ganz2022bigroc,
title={{BIGR}oC: Boosting Image Generation via a Robust Classifier},
author={Roy Ganz and Michael Elad},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2022},
url={https://openreview.net/forum?id=y7RGNXhGSR},
note={}
}
Preprint on ArXiv: 2108.03702
The entire code is contained in Colab jupiter notebook to facilitate the environment installation. Each such notebook is self-contained with the relevant package installation and explanations.
| File name | Content |
|---|---|
/CIFAR10 |
Notebooks for Section 5.1 - experimenting BIGRoC on CIFAR-10 image generators, both conditional and unconditional |
/ImageNet |
Notebooks for Section 5.2 - experimenting BIGRoC on ImageNet 128x128 & 256x256 image generators, both conditional and unconditional |
/Comparison |
Notebooks for Section 5.3 - experimenting BIGRoC on CIFAR-10 and ImageNet using SN-ResNetGAN |