This is part of a series of articles in AI Codecs. While digital media are transmitted in a wide variety of settings, the available codecs are "one-size-fits-all": they are hard-coded, and cannot be customized to particular use cases beyond high-level hyperparameter tuning [10]. In the last few years, deep learning has revolutionized many tasks such as machine translation, speech recognition, face recognition, natural language processing and photo-realistic image generation. Given unlabeled training data, deep learning based models generate new samples from the input data distribution [6]. These are called deep generative models and have powerful capabilities such as extracting features by learning a low-dimension feature representation of the input space and sampling to generate, restore, predict or compress data.