In this course, students will autonomously investigate recent research about machine learning techniques in the computer graphics area. Independent investigation for further reading, critical analysis, and evaluation of the topic are required.
- The participants have to present their topics in a talk (in English), which should last 30 minutes.
- The semi-final slides (PDF) should be sent one week before the talk; otherwise, the talk will be canceled.
- A short report (approximately 3-4 pages in the ACM SIGGRAPH TOG format (acmtog)) should be prepared and sent within two weeks after the talk. When you send the report, please send the final slides (PDF) together.
|10 July 2017||Kick-off meeting in room MI 02.13.010 at 16:00|
|15 September 2017||Deadline for sending an e-mail with 3 preferences|
|22 September 2017||Notification of assigned paper|
|16 Oct 2017||Tobias Bernecker||2016, Dong et al., Image Super-Resolution Using Deep Convolutional Networks, IEEE Trans. Pattern Anal. Mach. Intell.|
|23 Oct 2017||Palle Klewitz||2014, Goodfellow et al., Generative Adversarial Networks, Advances in Neural Information Processing Systems 27|
|23 Oct 2017||Haoran Chen||2015, Mnih et al., Human-Level Control Through Deep Reinforcement Learning, Nature|
|06 Nov 2017||Maximilian Werhahn||2017, Yi et al., Learning Hierarchical Shape Segmentation and Labeling from Online Repositories, ACM Trans. Graph.|
|canceled||Virendra Kumar Pathak||2017, Wang et al., O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis, ACM Trans. Graph.|
|canceled||Hyeon Su Kim||2016, Ruder et al., Artistic Style Transfer for Videos, arXiv.org|
(optional) 2015, Gatys et al., A Neural Algorithm of Artistic Style, arXiv.org
|06 Nov 2017||Artem Bishev||2017, Chu and Thuerey, Data-driven Synthesis of Smoke Flows with CNN-based Feature Descriptors, ACM Trans. Graph.|
|13 Nov 2017||Tuba Topaloglu||2017, Li et al., Deep Extraction of Manga Structural Lines, ACM Trans. Graph.|
|13 Nov 2017||Christoph Neuhauser||2017, Gharbi et al., Deep Bilateral Learning for Real-time Image Enhancement, ACM Trans. Graph.|
|20 Nov 2017||Maria Dreher||2015, Gryka et al., Learning to Remove Soft Shadows, ACM Trans. Graph.|
|20 Nov 2017||Felix Neumeyer||2017, Bako et al., Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings, ACM Trans. Graph.|
|canceled||Tobias Zengerle||2016, Peng et al., Terrain-adaptive Locomotion Skills Using Deep Reinforcement Learning, ACM Trans. Graph.|
|27 Nov 2017||Max Heimbrock||2017, Holden et al., Phase-functioned Neural Networks for Character Control, ACM Trans. Graph.|
|04 Dec 2017||Luca Klingenberg||2017, Karras et al., Audio-driven Facial Animation by Joint End-to-end Learning of Pose and Emotion, ACM Trans. Graph.|
|04 Dec 2017||Hans Rauer||2017, Suwajanakorn et al., Synthesizing Obama: Learning Lip Sync from Audio, ACM Trans. Graph.|
|11 Dec 2017||Reza Roustaei Khoshkbijari||2017, Dahl et al., Pixel Recursive Super Resolution, arXiv.org|
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