Master-Seminar – Deep Learning in Computer Graphics (IN2107, IN0014)

Benjamin Holzschuh, Hao Wei and Nils Thuerey

Content

In this course, students will autonomously investigate recent research about machine learning techniques in computer graphics. Independent investigation for further reading, critical analysis, and evaluation of the topic are required.

Requirements

Participants are required to first read the assigned paper and start writing a report. This will help you prepare for your presentation.

Attendance

• It is only allowed to miss two sessions. If you have to miss any, please let us know in advance, and write a one-page summary about the paper in your own words. Missing the third one means failing the seminar. 

Report

• A short report (4 pages max. excluding references in the ACM SIGGRAPH TOG format (acmtog) - you can download the precompiled latex template) should be prepared and sent two weeks after the talk, i.e., by 23:59 on Wednesday.
• Guideline: You can begin with writing a summary of the work you present as a start point; but, it would be better if you focus more on your own research rather than just finishing with the summary of the paper. We, including you, are not interested in revisiting the work done before; it is more meaningful if you make an effort to put your own reasoning about the work, such as pros and cons, limitation, possible future work, your own ideas for the issues, etc.
• For questions regarding your paper or feedback for a semi-final version of your report you can contact your advisor.

Presentation (slides)

• You will present your topic in English, and the talk should last 25 to 30 minutes. After that, a discussion session of about 10 minutes will follow.
• The slides should be structured according to your presentation. You can use any layout or template you like, but make sure to choose suitable colors and font sizes for readability.
• Plagiarism should be avoided; please do not simply copy the original authors' slides. You can certainly refer to them.
• The semi-final slides (PDF) should be sent one week before the talk, otherwise the talk will be canceled.
• We strongly encourage you to finalize the semi-final version as far as possible. We will take a look at the version and give feedback. You can revise your slides until your presentation.
• The final slides should be sent together with the report after the talk.

Topics

References

• Thuerey group: List of Publications (including Physics-based Deep Learning works)
• Book: Bishop, Pattern Recognition and Machine Learning
• Book: Hastie et al., The Elements of Statistical Learning
• Book/Online: Goodfellow et al., Deep Learning
• Online: Nielsen, Neural Networks and Deep Learning
• Online: Ruder, An Overview of Gradient Descent Optimization Algorithms