TeachingWs18DGM

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

Hauptseminar/Master Seminar:
Deep Generative Models WS2018

Team: Prof. Nassir Navab, Dr. Vasileios Belagiannis, Dr. Federico Tombari, Iro Laina, Magda Paschali, Leslie Casas, Oliver Scheel, Yida Wang, Helisa Dhamo

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

  • 28.06.2018 - Motivation Letters are welcomed.
  • 12.06.2018 - Preliminary meeting 21.06.2018 at 5:00pm, MI 03.13.010.

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

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
01 Anish Athalye et. al. Synthesizing Robust Adversarial Examples ICML 2018 https://arxiv.org/abs/1707.07397 - -
02 Anish Athalye et. al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples ICML 2018 https://arxiv.org/abs/1802.00420   -
03 Andrew Ilyas et. al. Black-box Adversarial Attacks with Limited Queries and Information ICML 2018 https://arxiv.org/abs/1804.08598 Oliver Paolo N.
04 Lars Mescheder et. al. Which Training Methods for GANs do actually Converge? ICML 2018 https://arxiv.org/abs/1801.04406 Azade Ali Abbas J.
05 Xiaoyu Lu et. al. Structured Variationally Auto-encoded Optimization ICML 2018 http://proceedings.mlr.press/v80/lu18c.html Leslie Julio O.
06 Junbo Zhao et. al. Adversarially Regularized Autoencoders arxiv https://arxiv.org/abs/1706.04223 - -
07 Jiawei Su et. al. One pixel attack for fooling deep neural networks arxiv https://arxiv.org/abs/1710.08864 Magda Jongwon L.
08 Karol Kurach et. al. The GAN Landscape: Losses, Architectures, Regularization, and Normalization arxiv https://arxiv.org/abs/1807.04720 Helisa Ivan P.
09 Mario Lucic, et. al. Are GANs Created Equal? A Large-Scale Study arxiv https://arxiv.org/abs/1711.10337 Vasilis Maria M.
10 Angjoo Kanazawa, et. al. Learning Category-Specific Mesh Reconstruction from Image Collections ECCV 2018 https://arxiv.org/abs/1803.07549 Yida Giorgio F.
11 Omid Poursaeed, et. al. Generative Adversarial Perturbations CVPR 2018 https://arxiv.org/abs/1712.02328 Leslie Abdelrahman E. 
12 Samaneh Azadi, et. al. Multi-Content GAN for Few-Shot Font Style Transfer CVPR 2018 https://arxiv.org/abs/1712.00516 Yida Wout B.
13 Ming-Yu Liu, et. al. Unsupervised Image-to-Image Translation Networks NIPS 2017 https://arxiv.org/abs/1703.00848 Federico Aadhithya S.
14 Long Chen, et. al. Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks CVPR 2018 https://arxiv.org/abs/1712.01928 Azade Mingyip C.
15 Xi Peng, et. al. Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation CVPR 2018 https://arxiv.org/abs/1805.09707 Iro Hanna K.
16 Minghua Zhang, et. al. An Unsupervised Model with Attention Autoencoders for Question Retrieval AAAI 2018 https://arxiv.org/abs/1803.03476 - -
17 Chaowei Xiao, et. al. Spatially Transformed Adversarial Examples ICLR 2018 https://arxiv.org/abs/1801.02612 Magda Nitin V.
18 Sashank J. Reddi, et. al. ON THE CONVERGENCE OF ADAM AND BEYOND ICLR 2018 https://openreview.net/forum?id=ryQu7f-RZ - -
19 Jonathan Ho and Stefano Ermon Generative Adversarial Imitation Learning NIPS 2016 https://arxiv.org/abs/1606.03476 Vasilis Raymond C.

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
17.10.2018 16:00 - 18:00 Lecture,
Are GANs Created Equal? A Large-Scale Study,
The GAN Landscape: Losses, Architectures, Regularization, and Normalization.
Maria M.,
Ivan P.
Slides
08.11.2018 16:00 - 18:00 One pixel attack for fooling deep neural networks,
Spatially Transformed Adversarial Examples,
Black-box Adversarial Attacks with Limited Queries and Information,
Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks,
Jongwon L.,
Nitin V.,
Paolo N.
Mingyip C.
 
15.11.2018 16:00 - 18:00 Generative Adversarial Perturbations,
Generative Adversarial Imitation Learning,
Which Training Methods for GANs do actually Converge?,
Multi-Content GAN for Few-Shot Font Style Transfer
Abdelrahman E.,
Raymond C.,
Ali Abbas J.,
Wout B.
 
22.11.2018 16:00 - 18:00 Unsupervised Image-to-Image Translation Networks,
Learning Category-Specific Mesh Reconstruction from Image Collections,
Structured Variationally Auto-encoded Optimisation
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Aadhithya S.,
Giorgio F.,
Julio O.,
Hanna K.
 

TeachingForm
Title: Deep Generative Models
Professor: Prof. Nassir Navab, Dr. Vasileios Belagiannis, Dr. Federico Tombari
Tutors: Iro Laina, Magda Paschali, Yida Wang
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2018WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 14 Nov 2018 - 19:41 - VasileiosBelagiannis

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