Masked Diffusion

Masked diffusion models are a class of generative models that leverage a masking mechanism, rather than additive noise, to learn data distributions and generate new samples. Current research focuses on improving efficiency and scalability, exploring connections to other generative models like autoregressive models and autoencoders, and applying masked diffusion to diverse tasks such as image and video generation, sound synthesis, and anomaly detection. This approach offers advantages in speed and memory efficiency compared to traditional diffusion models, leading to advancements in various fields including computer vision, natural language processing, and medical image analysis.