  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 to refine its recognition. We demonstrate this
post-processing algorithm on various image synthesis methods and show a
significant quantitative and qualitative improvement on CIFAR-10 and ImageNet.
Surprisingly, although BIGRoC is the first model agnostic among refinement
approaches and requires much less information, it outperforms competitive
methods. Specifically, BIGRoC improves the image synthesis best performing
diffusion model 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. Moreover, we conduct an
opinion survey, according to which humans significantly prefer our method's
outputs.
