Summer School on Missing Data, Augmentation, and Generative Models

Missing data is a common problem in image processing and in general AI based methods. The source can be, for example, occlusions in 3D computer vision problems, poorly dyed tissue in biological applications, missing data points in long-term observations, or perhaps there is just too little annotated data for a deep-learning model to properly converge. On this Ph. D. summer school, you will learn some of the modern approaches to handling the above-mentioned problems in a manner compatible with modern machine learning methodology.

Target group: Ph.D. students working with AI and images. The students are expected to have experience with programming in Python and a working knowledge of modern AI methods such as being able to set up UNet for image segmentation tasks.