Supplementary: CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks

Our approach leverages feature space for style modulation to adapt to the novel task. We train our model on various datasets to show the effectiveness of our approach in generating high-dimensional and diverse domains images in a streamed manner. Due to limited space, we could only demonstrate part of the generated images in the main paper(Sec.2.3). We inherit GAN architecture from "Which Training Methods for GANs do actually Converge?"(GP-GAN) We select GP-GAN architecture as it has been very successful in generating quality samples in high-dimensional spaces, by providing stable training.