  Conditional Generative Adversarial Networks (cGAN) generate realistic images
by incorporating class information into GAN. While one of the most popular
cGANs is an auxiliary classifier GAN with softmax cross-entropy loss (ACGAN),
it is widely known that training ACGAN is challenging as the number of classes
in the dataset increases. ACGAN also tends to generate easily classifiable
samples with a lack of diversity. In this paper, we introduce two cures for
ACGAN. First, we identify that gradient exploding in the classifier can cause
an undesirable collapse in early training, and projecting input vectors onto a
unit hypersphere can resolve the problem. Second, we propose the Data-to-Data
Cross-Entropy loss (D2D-CE) to exploit relational information in the
class-labeled dataset. On this foundation, we propose the Rebooted Auxiliary
Classifier Generative Adversarial Network (ReACGAN). The experimental results
show that ReACGAN achieves state-of-the-art generation results on CIFAR10,
Tiny-ImageNet, CUB200, and ImageNet datasets. We also verify that ReACGAN
benefits from differentiable augmentations and that D2D-CE harmonizes with
StyleGAN2 architecture. Model weights and a software package that provides
implementations of representative cGANs and all experiments in our paper are
available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
