Course 2020
| Course title: |
Deep Learning for Computer Vision: Seminal Work |
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| Course ID: |
263-5904-00L
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| Lecturers: |
Dr. Martin Oswald, Dr.
Zhaopeng Cui
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| Teaching assistants: |
Daniel Thul,
Ian Cherabier,
Mihai Dusmanu,
Marcel Geppert,
Sandro Lombardi,
Luca Cavalli,
Rémi Pautrat,
Taein Kwon,
Songyou Peng,
Denys Rozumnyi,
Katarina Tóthová,
Silvan Weder,
Zuoyue Li,
Paul-Edouard Sarlin
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| Venue: |
Mo 15-17h in CAB
G 57 |
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This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and
discuss the papers and gain an understanding of the most influential research in this area - both past and present.
The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key
contributions to the field of deep learning for vision (including a historical perspective as well as recent work).
Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as
well as how to give a scientific presentation and lead a discussion on their topic.
Each student chooses one paper from the provided collection to present during the course of the seminar. The
students will be supported in the preparation of their presentation by the seminar assistants.
News
- 24.02. Paper assignment has been published.
- 17.02. Paper assignment will open today at 5pm. Please choose your favorite papers in the Moodle course until the end of
Feb. 21 (this Friday).
- 17.02. The slides of today's introduction session are online now.
Important information
Each student will present one paper. Everybody is encouraged to read each paper before it is being presented and
engage in a discussion following the presentations. To foster interesting discussions, each paper will also be
assigned two "critics" who study the paper and prepare questions for the discussion. Each student will be graded
based on both their presentation (80%) and their participation in the assigned discussions (20%).
Attendance is required to pass the course (3 absences allowed).
Slides
Schedule (Tentative)
| Date |
Topic |
Presenter |
Critics |
Assistant |
| 17.02. |
Introduction |
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| 24.02. |
No seminar - Paper assignment will be announced on this page |
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| 02.03. |
01 Learning representations by
back-propagating
errors |
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Marcel Geppert |
| 02.03. |
02 Backpropagation applied to
handwritten zip code recognition |
Adrian K. |
Costanza Maria I., Frédéric O. |
Denys Rozumnyi |
| 02.03. |
03
ImageNet
Classification with Deep Convolutional Neural Networks |
Boyan D. |
Carla J., Florin V. |
Dr. Zhaopeng Cui |
| 09.03. |
04 U-Net: Convolutional Networks for Biomedical Image
Segmentation |
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|
Denys Rozumnyi |
| 09.03. |
05 Deep residual learning for image recognition |
Christian B. |
Simon H., Jiahao W. |
Mihai Dusmanu |
| 09.03. |
06 Xception: Deep Learning with Depthwise Separable
Convolutions
|
Valentin W. |
Matej S., Ali A. |
Luca Cavalli |
| 16.03. |
07 Dropout: a simple way to
prevent
neural networks from overfitting |
Erick T. |
Rishabh S., Yannick R. |
Daniel Thul |
| 16.03. |
08
Group
Normalization |
Frédéric O. |
Jiahao W., Boyan D. |
Zuoyue Li |
| 23.03. |
09 Visualizing and Understanding Convolutional Networks
| Yannick R. |
Valentin W., Erick T. |
Silvan Weder |
| 23.03. |
10 What Uncertainties Do We Need in Bayesian Deep Learning for
Computer Vision? |
Yannick S. |
Rishabh S. |
Rémi Pautrat |
| 30.03. |
11 Human-level control
through
deep re-inforcement learning |
Matej S. |
Erick T., Simon H. |
Luca Cavalli |
| 30.03. |
12
Conditional
Random Fields as Recurrent Neural Networks |
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|
Ian Cherabier |
| 06.04. |
13
Learning Convolutional Neural Networks for Graphs |
Simon H. |
Frédéric O., Yuqing C. |
Zuoyue Li |
| 06.04. |
14 Dynamic Routing Between Capsules |
Rishabh S. |
Boyan D., Matej S. |
Dr. Martin Oswald |
| 27.04. |
15
A Style-Based Generator Architecture for Generative Adversarial Networks |
Florin V. |
Dexin Y., Costanza Maria I. |
Rémi Pautrat |
| 27.04. |
16 Unpaired Image-to-Image Translation using Cycle-Consistent
Adversarial Networks |
Yuqing C. |
Yannick S., Valentin W. |
Songyou Peng |
| 04.05. |
17 Mask R-CNN |
Costanza Maria I. |
Yannick R., Adrian K., Carla J. |
Taein Kwon |
| 04.05. |
18 AtlasNet: A Papier-Mâché Approach to Learning 3D Surface
Generation |
Carla J. |
Yuqing C., Manuel B. |
Sandro Lombardi |
| 11.05. |
19 Working hard to know your neighbor's margins: Local
descriptor learning loss |
Dexin Y. |
Florin V., Georges P., Ali A. |
Paul-Edouard Sarlin |
| 11.05. |
20
DSAC-differentiable
RANSAC for Camera Localization |
Jiahao W. |
Manuel B., Christian B., Yannick S. |
Mihai Dusmanu |
| 18.05. |
21
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation |
Ali A. |
Adrian K., Dexin Y., Christian B. |
Katarina Tóthová |
| 18.05. |
22
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationsh |
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Sandro Lombardi |
| 25.05. |
23
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks |
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Daniel Thul |
| 25.05. |
24
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation |
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Silvan Weder |
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