  Generative Adversarial Networks (GANs) produce impressive results on
unconditional image generation when powered with large-scale image datasets.
Yet generated images are still easy to spot especially on datasets with high
variance (e.g. bedroom, church). In this paper, we propose various improvements
to further push the boundaries in image generation. Specifically, we propose a
novel dual contrastive loss and show that, with this loss, discriminator learns
more generalized and distinguishable representations to incentivize generation.
In addition, we revisit attention and extensively experiment with different
attention blocks in the generator. We find attention to be still an important
module for successful image generation even though it was not used in the
recent state-of-the-art models. Lastly, we study different attention
architectures in the discriminator, and propose a reference attention
mechanism. By combining the strengths of these remedies, we improve the
compelling state-of-the-art Fr\'{e}chet Inception Distance (FID) by at least
17.5% on several benchmark datasets. We obtain even more significant
improvements on compositional synthetic scenes (up to 47.5% in FID).
