One Class at a Time: Single-Class Batch Sampling for Training Class-Conditional GANs on Limited Data

Training conditional generative adversarial networks on limited data remains a challenging open problem, with severe mode collapse resulting from discriminator overfitting and insufficient per-class supervision. Existing approaches address this through data augmentation, regularisation, and knowledge sharing between classes, but the role of batch composition in shaping the discriminator's learned representations has received little attention. We investigate the effect of single-class batch sampling, wherein each training batch is composed exclusively of images from a single class, on conditional GAN training under limited data regimes. We argue that standard mixed-class batches allow the discriminator to exploit coarse inter-class differences as a shortcut for real/fake discrimination, reducing pressure to capture intra-class features and consequently weakening the signal provided to the generator. Single-class batches eliminate this shortcut, forcing the discriminator to attend to intra-class structure. Experiments across multiple limited data benchmarks demonstrate consistent improvements in quality and diversity. Our results suggest that batch composition is an overlooked but practically significant axis of design in conditional GAN training in limited data.