Title: DIFFUSED INSTANCE CONDITIONED GAN. Abstract: Recently, numerous data partitioning methods for generative adversarial networks has been developed for better distribution coverage on complex distribution. Most of these approaches aims to build fine-grained overlapping clusters in data manifold and condition both generator and discriminator with compressed representation about cluster. Although giving larger size of condition can be more informative, existing algorithms only utilize low dimension vector as condition due to dependency on clustering algorithm and unsupervised / self-supervised learning methods. In this work, we take a step towards using richer representation for cluster by utilizing diffusion based Gaussian mixture. Our analysis shows that we can derive continuous representation of cluster with Gaussian mixture when
noise scale is given. Moreover, unlike other counterparts, we do not need excessive computation for acquiring clustered representation. Experiments on multiple datasets show that our model produces better results compared to recent GAN models.