TeachingSs19DGM

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

Hauptseminar/Master Seminar:
Deep Generative Models SS2019

Team: Prof. Nassir Navab, Dr. Gustavo Carneiro, Azade Farshad, Magda Paschali, Helisa Dhamo, Oliver Scheel, Ashkan Khakzar, Mahdi Saleh, Leslie Casas, Artem Savkin

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: TBD
Course Language: English

Announcements

  • 17-01-2019: Motivation Letters are welcomed. You can send them to Azade Farshad (azade.farshad@tum.de).
  • 17-01-2019: Preliminary meeting 31.01.2019 at 4:00pm, MI 03.13.010.
  • 14-01-2019: The website is up!

Introduction

  • The recent advances of generative models in Deep Learning will be studied in the Seminar Course. Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), adversarial learning and learning with adversaries compose the main topics of the seminar. A set of papers will cover the aforementioned topics. One or two lectures will be also offered to support the seminar.

Registration

  • The registration is through the TUM Matching Platform (from 08.02.19 to 13.02.19).
  • If you have any questions regarding this seminar, feel free to contact Azade Farshad.

Requirements

In this 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.
  • 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 Ruixiang Zhang et. al. MetaGAN?: An Adversarial Approach to Few-Shot Learning NIPS 2018 https://papers.nips.cc/paper/7504-metagan-an-adversarial-approach-to-few-shot-learning.pdf Azade Silvia S.
2 Aaron van den Oord et. al. Neural Discrete Representation Learning NIPS 2017 https://arxiv.org/pdf/1711.00937.pdf Artem Faizan K.
3 Pouya Samangouei et. al. ExplainGAN?: Model Explanation via Decision Boundary Crossing Transformations ECCV 2018 http://openaccess.thecvf.com/content_ECCV_2018/papers/Nathan_Silberman_ExplainGAN_Model_Explanation_ECCV_2018_paper.pdf Magda Simon K.
4 Anish Athalye et. al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples ICML 2018 https://arxiv.org/pdf/1802.00420.pdf Magda Volodymyr F.
5 Xingjun Ma et. al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality ICLR 2018 https://arxiv.org/pdf/1801.02613.pdf    
6 Zelun Luo et. al. Label Efficient Learning of Transferable Representations across Domains and Tasks NIPS 2017 https://papers.nips.cc/paper/6621-label-efficient-learning-of-transferable-representations-acrosss-domains-and-tasks.pdf Mahdi Jonas B.
7 Xiuming Zhang et. al. Learning to Reconstruct Shapes from Unseen Classes NIPS 2018 https://papers.nips.cc/paper/7494-learning-to-reconstruct-shapes-from-unseen-classes.pdf Leslie Sergey M.
8 Jiaxuan You et. al. GraphRNN?: Generating Realistic Graphs with Deep Auto-regressive Models ICML 2018 http://proceedings.mlr.press/v80/you18a/you18a.pdf Helisa Paul M.
9 Ting-Chun Wang et. al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs CVPR 2018 http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_High-Resolution_Image_Synthesis_CVPR_2018_paper.pdf Helisa Felix A.
10 Tameem Adel et. al. Discovering Interpretable Representations for Both Deep Generative and Discriminative Models ICML 2018 http://proceedings.mlr.press/v80/adel18a/adel18a.pdf Ashkan Lukas F.
11 Carlos Florensa et. al. Automatic Goal Generation for Reinforcement Learning Agents ICML 2018 http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Oliver Dominik U.
12 Augustus Odena et. al. Is Generator Conditioning Causally Related to GAN Performance? ICML 2018 http://proceedings.mlr.press/v80/odena18a/odena18a.pdf Oliver Alessa G.
13 Rithesh Kumar et. al. Maximum Entropy Generators for Energy-Based Models arXiv 2019 https://arxiv.org/pdf/1901.08508.pdf Azade Shabnam S.
14 Chen et. al. InfoGAN?: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets NIPS 2016 https://arxiv.org/pdf/1606.03657.pdf Ashkan Hossain S.
15 Dilokthanakul et. al. Deep Unsupervised Clustering With Gaussian Mixture Variational Autoencoders arXiv 2017 https://arxiv.org/pdf/1611.02648.pdf    
16 Frosst et. al. Analyzing and Improving Representations with the Soft Nearest Neighbor Loss arXiv 2019 https://arxiv.org/pdf/1902.01889.pdf Artem Alexander S.

Schedule

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

Date Time Title Speaker Material
31.01.2019 16:00 - 17:00 Preliminary meeting Azade Farshad  
24.04.2019 16:00 - 18:00 Introductory Lecture Dr. Gustavo Carneiro  
08.05.2019 16:00 - 18:00 GraphRNN?: Generating Realistic Graphs with Deep Auto-regressive Models
Automatic Goal Generation for Reinforcement Learning Agents
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Paul M.
Dominik U.
Felix A.
Alexander S.
 
15.05.2019 16:00 - 18:00 Label Efficient Learning of Transferable Representations across Domains and Tasks
Neural Discrete Representation Learning
Maximum Entropy Generators for Energy-Based Models
Is Generator Conditioning Causally Related to GAN Performance?
Jonas B.
Faizan K.
Shabnam S.
Alessa G.
 
22.05.2019 16:00 - 18:00 ExplainGAN??: Model Explanation via Decision Boundary Crossing Transformations
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Simon K.
Lukas F.
Volodymyr F.
Hossain S.
 
05.06.2019 16:00 - 18:00 MetaGAN?: An Adversarial Approach to Few-Shot Learning
Learning to Reconstruct Shapes from Unseen Classes
Silvia S.
Sergey M.
 

TeachingForm
Title: Deep Generative Models
Professor: Prof. Nassir Navab, Dr. Gustavo Carneiro
Tutors: Azade Farshad, Magda Paschali, Helisa Dhamo, Oliver Scheel, Ashkan Khakzar, Mahdi Saleh, Leslie Casas
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2019SoSe
Abstract:  


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