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. Such machines' ultimate goal is to match the distributions of the given training images and the synthesized ones. 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.

翻译：近年来,机器学习界对图像合成的兴趣有了显著提高,引进了各种深厚的基因化模型和培训模型的方法。这些机器的最终目标是匹配特定培训图像和合成图像的分布。在这项工作中,我们提出一种通用的模型 -- -- 不可知性技术,以提高图像质量和通过任何基因化模型获得的生成图像的分布准确性。我们称为BIGRoC(通过一个强大的分类器促进图像生成)的方法是基于一个后处理程序,由某个强健的分类器指导,不需要对基因化模型进行额外培训。鉴于一个综合图像,我们提议通过预测的关于强健分类器的梯度步骤来更新它,以改进其认知。我们用各种图像合成方法展示了这种后处理算法,并展示了生成图像在数量和质量上的重大改进。