  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.
