08/13/2019

Exam 1 Viewing on 13-08-2019 at 1000 hours ( Zi 3.041 )

News

On Wednesday, 24.07.2019, 12:15, we will be available for questions (we will meet in front of HS3+4, in case they are booked we will be in the neigboring building (Endenicher Allee 19A) in room 3.035b).

 

Please send your questions via mail to both mw@cs.uni-bonn.de and majumder@cs.uni-bonn.de until Tuesday, 12:00 to allow a more efficient handling.

 

Please also feel free to send us questions earlier. We will collect them in a pdf that will be available to you in the documents section of the course-webpage where we also try to add the answers as early as possible.

Lecture: Advanced Deep Learning for Graphics

Course

  • Lecturer(s):
  • Start: 10.04.2019
  • Dates: Wed. 12:15 - 13:45 HS 4
  • Course number: MA-INF 2217
  • Curriculum: Master
  • Effort: 2.0 SWS
  • Exams: 25.7., 14:00-17:00, HS3+4,5+6,7, 19.9., 9:00-12:00, HS5+6,7

Exercises

  • Tutor(s):
  • Start: 27.4.
  • Dates: see BASIS and via appointment (with Soumajit Majumder)

Description

This course focuses on cutting-edge Deep Learning techniques for computer graphics. After a brief review of CNNs the focus will be laid on autoencoders, generative models and the extension of these methods to graph- and manifold-structured data.  Applications discussed will include inverse problems in computer graphics and the synthesis of models including data completion and super-resolution.

The course will build upon the basics of machine learning as well as fundamentals and basic architectures of neural networks. Therefore, it is highly recommended to have taken Deep Learning for Visual Recognition or a similar course as a prerequisite. Exercises will be a mix of theory and practical (Python).

 

 

 

Slides

Assignment Sheets

Exercise 1: Autoencoder
Assignment sheet  (PDF document, 141 KB)
Exercise 2: ImageSegmentation
Assignment sheet  (PDF document, 527 KB)
Exercise 3: Superresolution
Assignment sheet  (PDF document, 689 KB)
Exercise 4: 3DDeepLearning
Assignment sheet  (PDF document, 134 KB)

Additional Documents