Conditional image generation lies at the heart of computer vision and conditional generative adversarial networks (cGAN) have recently become the method of choice for this task, owing to their superior performance. The focus so far has largely been on performance improvement, with little effort in making cGANs more robust to noise. However, the regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGANs unreliable for real-world applications. In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in the target space of the model to address the issue. Specifically, we augment the generator with an unsupervised pathway, which promotes the outputs of the generator to span the target manifold, even in the presence of intense noise. We prove that RoCGAN share similar theoretical properties as GAN and establish with both synthetic and real data the merits of our model. We perform a thorough experimental validation on large scale datasets for natural scenes and faces and observe that our model outperforms existing cGAN architectures by a large margin. We also empirically demonstrate the performance of our approach in the face of two types of noise (adversarial and Bernoulli).

中文翻译：

---

RoCGAN：健壮的条件 GAN

条件图像生成是计算机视觉的核心，条件生成对抗网络 (cGAN) 由于其卓越的性能，最近已成为该任务的首选方法。到目前为止，重点主要放在性能改进上，几乎没有努力使 cGAN 对噪声更鲁棒。然而，（生成器的）回归可能会导致输出中出现任意大的错误，这使得 cGAN 在实际应用中不可靠。在这项工作中，我们引入了一种新的条件 GAN 模型，称为 RoCGAN，它利用模型目标空间中的结构来解决这个问题。具体来说，我们使用无监督路径增强生成器，即使在存在强烈噪声的情况下，它也会促进生成器的输出跨越目标流形。我们证明 RoCGAN 与 GAN 具有相似的理论特性，并利用合成数据和真实数据建立了我们模型的优点。我们对自然场景和人脸的大规模数据集进行了彻底的实验验证，并观察到我们的模型在很大程度上优于现有的 cGAN 架构。我们还凭经验证明了我们的方法在面对两种类型的噪声（对抗性和伯努利）时的性能。