Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise or leveraging structure in the output space of the model. The end-to-end regression (of the generator) might lead to arbitrarily large errors in the output, which is unsuitable for the application of such networks to real-world systems. In this work, we introduce a novel conditional GAN, called RoCGAN, which adds implicit constraints to address the issue. Our proposed model augments the generator with an unsupervised pathway, which encourages the outputs of the generator to span the target manifold even in the presence of large amounts of noise. We prove that RoCGAN shares similar theoretical properties as GAN and experimentally verify that the proposed model outperforms existing state-of-the-art cGAN architectures by a large margin in a variety of domains including images from natural scenes and faces.

该研究介绍了一种名为RoCGAN的新型条件GAN模型，该模型在图像生成的任务中表现优异，并在面临严重噪声的情况下使生成器输出的图像更接近目标空间，实验结果表明，RoCGAN在各种领域的表现都远优于现有的最先进的cGAN架构。

鲁棒性条件生成对抗网络