Non Autoregressive Generative

Non-autoregressive generative models aim to create data samples (images, text, sequences) in parallel, unlike autoregressive models which generate sequentially, one element at a time. Current research focuses on improving the quality and diversity of generated samples using techniques like masked modeling with enhanced sampling schemes, incorporating adversarial training (GANs), and employing auxiliary models (e.g., Token-Critic) to guide the generation process. These advancements offer significant speed improvements over autoregressive methods, making them attractive for real-time applications such as recommendation systems and medical image analysis, while also addressing challenges like exposure bias and sparse data.