  Conditional image generation is the task of generating diverse images using
class label information. Although many conditional Generative Adversarial
Networks (GAN) have shown realistic results, such methods consider pairwise
relations between the embedding of an image and the embedding of the
corresponding label (data-to-class relations) as the conditioning losses. In
this paper, we propose ContraGAN that considers relations between multiple
image embeddings in the same batch (data-to-data relations) as well as the
data-to-class relations by using a conditional contrastive loss. The
discriminator of ContraGAN discriminates the authenticity of given samples and
minimizes a contrastive objective to learn the relations between training
images. Simultaneously, the generator tries to generate realistic images that
deceive the authenticity and have a low contrastive loss. The experimental
results show that ContraGAN outperforms state-of-the-art-models by 7.3% and
7.7% on Tiny ImageNet and ImageNet datasets, respectively. Besides, we
experimentally demonstrate that contrastive learning helps to relieve the
overfitting of the discriminator. For a fair comparison, we re-implement twelve
state-of-the-art GANs using the PyTorch library. The software package is
available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
