Master-Seminar – Machine Learning in Graphics
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.
|24.03.2017||Deadline for sending an e-mail with 3 preferences|
|31.03.2017||Notification of assigned paper|
|24 Apr 2017||Michael||2016, Dong et al., Image Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence|
|24 Apr 2017||Thomas||2017, Dahl et al., Pixel Recursive Super Resolution, arXiv.org|
|01 May 2017||No talk||May Day (Maifeiertag)|
|08 May 2017||Elisabeth||2016, Yan et al., Automatic Photo Adjustment Using Deep Neural Networks, ACM Trans. Graph.|
|08 May 2017||Jonas||2015, Gryka et al., Learning to Remove Soft Shadows, ACM Trans. Graph.|
|15 May 2017||Julius||2014, Goodfellow et al., Generative Adversarial Networks, Advances in Neural Information Processing Systems 27 (NIPS 2014)|
|15 May 2017||Simon||2016, Ruder et al., Artistic Style Transfer for Videos, arXiv.org|
(optional) 2015, Gatys et al., A Neural Algorithm of Artistic Style, arXiv.org
|22 May 2017||Anna||2015, Kalantari et al., A Machine Learning Approach for Filtering Monte Carlo Noise, ACM Trans. Graph.|
|22 May 2017||Hans Theobald||2016, Ren et al., Image Based Relighting Using Neural Networks, ACM Trans. Graph.|
|29 May 2017||Oliver Jamal||2017, Zheng and Zheng, NeuroLens: Data-Driven Camera Lens Simulation Using Neural Networks, Computer Graphics Forum|
|29 May 2017||Moritz||2015, Ladický et al., Data-driven Fluid Simulations Using Regression Forests, ACM Trans. Graph.|
|05 Jun 2017||No talk||Whit Monday (Pfingstmontag)|
|12 Jun 2017||Eric||2017, Kim et al., Category-Specific Salient View Selection via Deep Convolutional Neural Networks, Computer Graphics Forum|
|12 Jun 2017||Lukas||2015, Mnih et al., Human-Level Control Through Deep Reinforcement Learning, Nature|
|19 Jun 2017||Sebastian||2015, Guo et al., 3D Mesh Labeling via Deep Convolutional Neural Networks, ACM Trans. Graph.|
|19 Jun 2017||Benedikt||2016, Simo-Serra et al., Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup, ACM Trans. Graph.|
|26 Jun 2017||Gerhard‑Mathias||2016, Nishida et al., Interactive Sketching of Urban Procedural Models, ACM Trans. Graph.|
|26 Jun 2017||Florian||2016, Zeng et al., 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions, arXiv.org|
|03 Jul 2017||Niklas||2016, Peng et al., Terrain-adaptive Locomotion Skills Using Deep Reinforcement Learning, ACM Trans. Graph.|
|03 Jul 2017||Jan||2016, Holden et al., A Deep Learning Framework for Character Motion Synthesis and Editing, ACM Trans. Graph.|
- 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