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 PhD 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.

This summer school will introduce the state-of-the-art for handling too little or missing data in image processing tasks. The topics include data augmentation, density estimation, and generative models. The course will include project work, where the participants make a small programming project relating their research to the summer school’s topics.

The summer school is the fifteenth summer school jointly organized by DIKU, DTU, and AAU. DIREC is co-sponsor of the PhD school.