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: MI 03.13.010
Time: 14:00 - 15:30
Course Language: English

Announcements

  • 19.10.2017 - The slides from the GAN lecture are now online.
  • 26.09.2017 - The schedule is online (please check bellow).
  • 24.09.2017 - The papers have been now assigned (please check bellow).
  • 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 Onur E.
3 Mirza, Mehdi, and Simon Osindero Conditional generative adversarial nets. arXiv 2014 https://arxiv.org/abs/1411.1784 Federico Yasaman R.
4 Arjovsky, Martin et al. Wasserstein gan arXiv 2017 https://arxiv.org/abs/1701.07875 Vasilis Alexander R.
5 Nowozin, Sebastian et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NIPS 2016 https://arxiv.org/abs/1606.00709 Leslie Maximilian M.
6 Larsen, Anders Boesen Lindbo, et al. Autoencoding beyond pixels using a learned similarity metric ICML 2016 https://arxiv.org/abs/1512.09300 Kristians Kishan S.
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 Chengnan Y.
8 Lample , Guillaume et al. Fader Networks: Manipulating Images by Sliding Attributes arXiv 2017 https://arxiv.org/abs/1706.00409 Oliver Tom D.
9 Donahue, Jeff, et al. Adversarial feature learning ICLR 2017 https://arxiv.org/abs/1605.09782 Federico Mohd Yawar N. S.
10 Dumoulin, Vincent, et al. Adversarially learned inference ICML 2017 https://arxiv.org/abs/1606.00704 Iro Enes T.
11 Reed, Scott, et al. Generative adversarial text to image synthesis ICML 2016 https://arxiv.org/abs/1605.05396 Kristians Mohammadamin B.
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 Kashif J.
13 Tzeng, Eric, et al. Adversarial discriminative domain adaptation CVPR 2017 https://arxiv.org/abs/1702.05464 Vasilis Nikolai M.

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 MI 03.13.010 V. Belagiannis Introduction to Generative Adversarial Networks (GANs), Slides, Code
07.11.2017 14:00-15:30 MI 03.13.010 Onur E., Yasaman R. Unsupervised representation learning with deep convolutional generative adversarial networks, Conditional generative adversarial nets
21.11.2017 14:00-15:30 MI 03.13.010 Alexander R., Maximilian M. Wasserstein gan, f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
05.12.2017 14:00-15:30 MI 03.13.010 Kishan S., Chengnan Y. Autoencoding beyond pixels using a learned similarity metric, Generating images with perceptual similarity metrics based on deep networks
19.12.2017 14:00-15:30 MI 03.13.010 Tom D., Mohd Yawar N. S. Fader Networks: Manipulating Images by Sliding Attributes, Adversarial feature learning
16.01.2018 14:00-15:30 MI 03.13.010 Enes T., Mohammadamin B. Adversarially learned inference, Generative adversarial text to image synthesis
23.01.2018 14:00-15:30 MI 03.13.010 Kashif J., Nikolai M. Unpaired image-to-image translation using cycle-consistent adversarial networks, Adversarial discriminative domain adaptation



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 r1.24 - 24 Oct 2017 - 14:29 - IroLaina

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