Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can produce subpar fake images due to limited training data. Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling. We present a novel NDA approach called Dual-NDA specifically tailored for CcGANs to address this problem. Dual-NDA employs two types of negative samples: visually unrealistic images generated from a pre-trained CcGAN and label-inconsistent images created by manipulating real images' labels. Leveraging these negative samples, we introduce a novel discriminator objective alongside a modified CcGAN training algorithm. Empirical analysis on UTKFace and Steering Angle reveals that Dual-NDA consistently enhances the visual fidelity and label consistency of fake images generated by CcGANs, exhibiting a substantial performance gain over the vanilla NDA. Moreover, by applying Dual-NDA, CcGANs demonstrate a remarkable advancement beyond the capabilities of state-of-the-art conditional GANs and diffusion models, establishing a new pinnacle of performance. Our codes can be found at https://github.com/UBCDingXin/Dual-NDA.

翻译：连续条件生成对抗网络（CcGANs）能够基于连续标量变量（称为回归标签）进行生成式建模。然而，由于训练数据有限，它们可能生成次优的虚假图像。尽管负数据增强（NDA）通过向真实训练图像引入异常，引导GAN远离低质量输出，有效增强了无条件和类条件GAN，但其对CcGAN的影响有限——因为该方法无法复现CcGAN采样过程中可能出现的负样本。针对这一问题，我们提出了一种专为CcGAN设计的全新NDA方法——双负数据增强（Dual-NDA）。Dual-NDA采用两种类型的负样本：通过预训练CcGAN生成的视觉失真图像，以及通过篡改真实图像标签创建的标签不一致图像。利用这些负样本，我们引入了一种新颖的判别器目标函数及改进的CcGAN训练算法。在UTKFace和Steering Angle数据集上的实证分析表明，Dual-NDA能持续提升CcGAN生成图像的视觉保真度和标签一致性，相较于原始NDA展现出显著的性能优势。此外，应用Dual-NDA后，CcGAN在性能上超越了当前最先进的条件GAN和扩散模型，树立了新的性能标杆。我们的代码可在https://github.com/UBCDingXin/Dual-NDA获取。