Master-Seminar – Deep Learning in Computer Graphics (IN2107, IN0014)
In this course, students will autonomously investigate recent research about machine learning techniques in the field of computer graphics. Independent investigation for further reading, critical analysis, and evaluation of the topic are required.
- It is only allowed to miss a single time-slot. Missing a second one means failing the seminar. If you have to miss any, please let us know in advance.
- The participants have to present their topics in a talk (in English), which should last 30 minutes. Don't put too many technical details into the talk, make sure the audience gets the paper's main idea. Be prepared to answer questions regarding the technical details, you could prepare backup slides for that.
- Afterwards, a short discussion session will follow.
- The slides should be structured according to your presentation. You can use any layout or template you like.
- Plagiarism is important; 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.
- Be ready in advance. We suggest testing the machines you are going to use before the lecture starts. You can bring your laptop or ask us one (also any converter you need for the projector) in advance. A laser pointer will be provided, so you can use if you want.
- 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 within two weeks after the talk, i.e., by 23:59 on Monday. When you send the report, please send the final slides (PDF) as well.
- 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.
|18 September 2018||Deadline for sending an e-mail with 3 preferences|
|21 September 2018||Notification of assigned paper|
|15 October 2018||Preparation week: No lecture!|
|22 October 2018||The first talk|
|22 Oct 2018||Sandra Grujovic||2017, Bako et al., Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings, ACM Trans. Graph.|
|22 Oct 2018||Michael Sorg||2017, Dahl et al., Pixel Recursive Super Resolution, arXiv.org|
|Canceled||James Li||2017, Wang et al., O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis, ACM Trans. Graph.|
|29 Oct 2018||Peng Chen||2017, Yi et al., Learning Hierarchical Shape Segmentation and Labeling from Online Repositories, ACM Trans. Graph.|
|05 Nov 2018||Alexander Potapov||2014, Goodfellow et al., Generative Adversarial Networks, Advances in Neural Information Processing Systems 27|
|05 Nov 2018||Markus Gögele||2016, Ruder et al., Artistic Style Transfer for Videos, GCPR|
(optional) 2015, Gatys et al., A Neural Algorithm of Artistic Style, arXiv.org
|12 Nov 2018||Simon Anlauff||2015, Mnih et al., Human-Level Control Through Deep Reinforcement Learning, Nature|
|Canceled||Florian Dollinger||2017, Gharbi et al., Deep Bilateral Learning for Real-time Image Enhancement, ACM Trans. Graph.|
|19 Nov 2018||Luca Sinn||2017, Li et al., Deep Extraction of Manga Structural Lines, ACM Trans. Graph.|
|19 Nov 2018||Johannes Rohwer||2018, Simo-Serra et al., Mastering Sketching: Adversarial Augmentation for Structured Prediction, ACM Trans. Graph.|
|26 Nov 2018||Felix Trost||2016, Peng et al., Terrain-adaptive Locomotion Skills Using Deep Reinforcement Learning, ACM Trans. Graph.|
|Canceled||Dirk Münzenmaier||2017, Holden et al., Phase-functioned Neural Networks for Character Control, ACM Trans. Graph.|
|03 Dec 2018||Henri Rößler||2017, Suwajanakorn et al., Synthesizing Obama: Learning Lip Sync from Audio, ACM Trans. Graph.|
|03 Dec 2018||Timm Knörle||2018, Kim et al., Deep Video Portraits, ACM Trans. Graph.|
|10 Dec 2018||Jakob Englhauser||2017, Karras et al., Audio-driven Facial Animation by Joint End-to-end Learning of Pose and Emotion, ACM Trans. Graph.|
|Canceled||Jean Vieira Filho||2018, Bailey et al., Fast and Deep Deformation Approximations, ACM Trans. Graph.|
You can access the papers through TUM library's eAccess.
- Book: Bishop, Pattern Recognition and Machine Learning
- Book: Hastie et al., The Elements of Statistical Learning
- Online: Nielsen, Neural Networks and Deep Learning
- Online: Ruder, An Overview of Gradient Descent Optimization Algorithms