Mask Distribution

Mask distribution research focuses on effectively handling missing or incomplete data in various applications, primarily image processing and machine learning. Current efforts center on developing robust models, such as diffusion probabilistic models and generative adversarial networks (GANs), that can accurately predict or generate missing information regardless of the pattern of missing data (the "mask"). This research is crucial for improving the performance of computer vision tasks like image inpainting and object detection, as well as addressing challenges in handling incomplete datasets across diverse fields. The ability to generalize across different mask distributions is a key objective, leading to more reliable and versatile algorithms.