Deep Generative Models WS2018
Team: Prof. Nassir Navab
, Dr. Vasileios Belagiannis
, Dr. Federico Tombari
, Iro Laina
, Magda Paschali
, Leslie Casas
, Oliver Scheel
, Yida Wang
, Helisa Dhamo
Advanced Seminar Course Module IN0014
Master Seminar Module IN2107
Seminar Module IN4826
Location: MI 03.13.010
- 28.06.2018 - Motivation Letters are welcomed.
- 12.06.2018 - Preliminary meeting 21.06.2018 at 5:00pm, MI 03.13.010.
- The recent advances of generative models in Deep Learning will be studied in the Seminar Course. Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), adversarial learning and learning with adversaries compose the main topics of the seminar. A set of papers will cover the aforementioned topics. One or two lectures will be also offered to support the seminar.
In this Seminar (formerly Hauptseminar), each student is asked to select one paper from a list. In order to successfully complete the seminar, participants have to fulfill these requirements:
- Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
- Written Report: A document of maximum 8 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
- Attendance: Participants have to participate actively in all seminar sessions.
The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. Part of the discussions outcome will be included in the report too. The report must include all references used and must be written completely in your own words
. Copy and paste will not be tolerated. Both report and presentation have to be done in English
List of Publications
The schedule for the paper presentations will be announced here during the semester. Each student is required to attend all presentations.