Image Distribution

Image distribution research focuses on understanding and manipulating the statistical properties of image data, aiming to improve generative models and enhance various computer vision tasks. Current research emphasizes leveraging diffusion models, generative adversarial networks (GANs), and normalizing flows to learn and manipulate these distributions, often focusing on techniques to mitigate the curse of dimensionality and address issues like data scarcity and distribution shifts across domains. This work has significant implications for improving image generation, medical image analysis, and robust computer vision systems by enabling more accurate modeling of real-world image data and facilitating better handling of noisy or incomplete information.