TeachingWs17DGM

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Hauptseminar/Master Seminar:
Deep Generative Models

Prof. Nassir Navab, Dr. Vasileios Belagiannis, Dr. Federico Tombari, Iro Laina, Christian Rupprecht, Leslie Casas, Kristians Diaz

Type: Advanced Seminar Course Module IN0014
Type: Master Seminar Module IN2107
Type: Seminar Module IN4826
SWS: 2+0
ECTS: 5 Credits
Location: TBD
Time: 14:00 - 15:30
Course Language: English

Announcements

  • 14-07-2017 The registration in TUM Matching is now open (14.07.2017 - 19.07.2017).
  • 12.07.2017 - The paper list is online (please check bellow).
  • Preliminary meeting: Room 03.10.11 on Thursday 06.07.2017 at 1pm.

Introduction

  • The recent advances of generative models in Deep Learning will be studied in the Seminar Course. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) compose the main topic of interest. A list of papers will be presented to understand the fundamentals of GANs, as well as, their variations (e.g. DCGAN, cGAN, wGAN and etc.). There will be a few application-oriented papers, including combinations with VAEs.

Registration

  • Preliminary Meeting: Room 03.10.11 on Thursday 06.07.2017 at 1pm.
  • The registration was performed though the TUM Matching Platform.

Requirements

In this Master Seminar (formerly Hauptseminar), each student is asked to select one paper from a list. In order to successfully complete the seminar, participants have to fulfill these requirements:

  • Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.zip.
  • Written Report: A document of maximum 8 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
  • Attendance: Participants have to participate actively in all seminar sessions.

The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. Part of the discussions outcome will be included in the report too. The report must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both report and presentation have to be done in English.

List of Publications

Nr Authors Title Conference Link Tutor Student
1 Goodfellow, Ian, et al. Generative adversarial networks NIPS 2014 https://arxiv.org/abs/1406.2661 Vasilis  
2 Radford, Alec, et al. Unsupervised representation learning with deep convolutional generative adversarial networks ICLR 2016 https://arxiv.org/abs/1511.06434 Iro  
3 Mirza, Mehdi, and Simon Osindero Conditional generative adversarial nets. arXiv 2014 https://arxiv.org/abs/1411.1784 Federico  
4 Arjovsky, Martin et al. Wasserstein gan arXiv 2017 https://arxiv.org/abs/1701.07875 Vasilis  
5 Nowozin, Sebastian et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NIPS 2016 https://arxiv.org/abs/1606.00709 Leslie  
6 Larsen, Anders Boesen Lindbo, et al. Autoencoding beyond pixels using a learned similarity metric ICML 2016 https://arxiv.org/abs/1512.09300 Kristians  
7 Dosovitskiy, Alexey, and Thomas Brox Generating images with perceptual similarity metrics based on deep networks NIPS 2016 https://arxiv.org/abs/1602.02644 Leslie  
8 Lample , Guillaume et al. Fader Networks: Manipulating Images by Sliding Attributes arXiv 2017 https://arxiv.org/abs/1706.00409 Oliver  
9 Donahue, Jeff, et al. Adversarial feature learning ICLR 2017 https://arxiv.org/abs/1605.09782 Federico  
10 Dumoulin, Vincent, et al. Adversarially learned inference ICML 2017 https://arxiv.org/abs/1606.00704 Iro  
11 Reed, Scott, et al. Generative adversarial text to image synthesis ICML 2016 https://arxiv.org/abs/1605.05396 Kristians  
12 Zhu, Jun-Yan, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks arXiv 2017 https://arxiv.org/abs/1703.10593 Christian  
13 Tzeng, Eric, et al. Adversarial discriminative domain adaptation CVPR 2017 https://arxiv.org/abs/1702.05464 Vasilis  

Schedule

The schedule for the paper presentations will be announced here during the semester. Each student is required to attend all presentations.

Date Time Place Speaker Material
06.07.2017 13:00 - 14:00 MI 03.10.11 V. Belagiannis Preliminary meeting, Slides
17.10.2017 14:00 - 15:30 tba V. Belagiannis Introduction to Generative Adversarial Networks (GANs)
07.11.2017 14:00 - 15:30 tba tba Paper Presentation
21.11.2017 14:00 - 15:30 tba tba Paper Presentation
05.12.2017 14:00 - 15:30 tba tba Paper Presentation
19.12.2017 14:00 - 15:30 tba tba Paper Presentation
16.01.2018 14:00 - 15:30 tba tba Paper Presentation
23.01.2018 14:00 - 15:30 tba tba Paper Presentation



TeachingForm
Title: Deep Generative Models
Professor: Prof. Nassir Navab, Dr. Vasileios Belagiannis, Dr. Federico Tombari
Tutors: Iro Laina
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2017WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.15 - 19 Aug 2017 - 06:33 - VasileiosBelagiannis

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