Modern Deep Learning has fundamentally changed Artificial Intelligence. Novel applications as well as significant improvements to old problems continue to appear at a staggering rate. Especially the areas of image and video understanding, retrieval, and synthesis have seen tremendous improvements and even the human baseline has been outperformed in several difficult applications.
The algorithms and the fundamental research in deep Machine Learning and Computer Vision that are driving this revolution are improving at an ever-increasing rate. The goal of this practical lab is, therefore, to give students hands-on experience with the state-of-the-art in this field of research. We will work on current problems in Computer Vision and Machine Learning and build on current algorithms to practically implement novel solutions. Consequently, the practical is also a good opportunity to take a close look at this area of research and prepare for a potential future final thesis.
Topics include but are not limited to:
* Image & video synthesis
* Visual superresolution and Image completion
* Artistic style transfer
* Interpretability of deep models
* Visual object classification, detection, tracking
* Self-supervised learning
* Metric and representation learning
* Modern deep learning approaches, such as transformers and self-attention, invertible neural networks, etc.
* …
Process
Students will work on a selected project. They will study existing approaches and then implement and evaluate an algorithm in close collaboration with a teaching assistant.
Requirements
Prior exposition to the foundations of machine learning and solid programming skills. Previous experience in deep learning is a plus.
- Teacher: Ursula Fantauzzo
- Teacher: Timo Milbich
- Teacher: Björn Ommer