Current Topics in Deep Learning

In this seminar recent deep learning papers will be presented and discussed. The goal is to dive deeper into deep learning topics beyond what can be covered in a lecture and also to give an insight in recent research topics in our lab. To name some of the topics: vision transformer and other recent large-scale models, implicit shape models and multi-object tracking. Below there is a list of papers of which you can choose a paper you want to present. It is also possible to suggest another cool paper in one of the topics.

Recommended previous knowledge

• B.Inf.1236 Machine Learning
• B.Inf.1237 Deep Learning for Computer Vision (the seminar can accompany lecture in the same term)

Examination

• Oral presentation (approx. 30 min.) and term paper (max. 5000 words)
• Examination requirements
  • Knowledge in a specific field of machine learning
  • Ability to present the acquired knowledge in a both orally and in a written report

Organization

There will be a starting session where the topics are presented. In the following week we give you an introduction in how to present and give feedback. In the next weeks, in each weekly session, one paper will be presented (25 minutes) and subsequently discussed (~15 minutes) and the other slot will be a trial presentation of the presenter of the following week.

Our plan is to hold this seminar in presence in room IfI 2.101 on Mondays 10:00 - 12:00.

If you are interested in this course, please register at Stud.IP.

Schedule