We offer a method for one-shot image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled Magic, samples structured gradients from a pre-trained quasi-robust classifier to better preserve the input semantics while preserving its classification accuracy, thereby guaranteeing credibility in the synthesis. Unlike current methods that use complex primitives to supervise the process or use attention maps as a weak supervisory signal, Magic aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior. Magic implements a series of manipulations with a single framework achieving shape and location control, intense non-rigid shape deformations, and copy/move operations in the presence of repeating objects and gives users firm control over the synthesis by requiring simply specifying binary guide masks. Our study and findings are supported by various qualitative comparisons with the state-of-the-art on the same images sampled from ImageNet and quantitative analysis using machine perception along with a user survey of 100+ participants that endorse our synthesis quality.

本研究提出了一种单张图像的综合方法，利用一个配备有强正则化器的准鲁棒分类器反演图像，从而实现对单张图像的操纵；该方法采样来自预训练的准鲁棒分类器的结构梯度，以更好地保留输入样本的语义，同时保证合成图像的分类准确性，实现形状、位置、非刚性形变等多种控制操作。

MAGIC: 通过反转准鲁棒分类器的掩模引导图像合成