Machine Vision and Learning - Master Practical
- Beschreibung
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.
* …In addition, there will be also a lecture on “Computer Vision and Deep Learning” in the following semester: ommer-lab.com/teaching/
- Institut
- Institut für Informatik
- Dozierende
- Kursadministration
- Externe Homepage
- https://ommer-lab.com/teaching/
- Kursteilnehmer:innen
- 6 von 6
- Zentralanmeldung
- Masterpraktika
- Direkte Anmeldung
Mo 24 Okt 2022 00:00 – Fr 31 Mär 2023 23:59
Abmeldung nur bis Fr 31 Mär 2023 23:59
- Anweisungen zur Bewerbung
Please shortly list your prior expertise related to Machine Learning and Computer Vision and any related courses you may have already attended. In case there are any topics that you find particularly interesting, you can also let us know.
- Material
Das Kursmaterial ist nur für Mitglieder des Kurses einsehbar, also z.B. für Teilnehmer:innen, Tutor:innen, Korrektor:innen und Verwalter:innen.