Course 2019
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
- 15.04. Paper #22 (AtlasNet) moved from 20.05. to 29.04. There will be no seminar on the 20.05.
- 04.03. Please upload the slides in the Moodle
course after your presentation.
- 04.03. Paper assignment has been updated: the presentation for paper #21 will be cancelled on May 20th.
- 20.02. Paper assignment has been released.
- 18.02. Paper assignment will open today at 3pm. Please choose your favorite papers in the Moodle course until the end of
tomorrow (19.02.).
- 18.02. 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 |
| 18.02. |
Introduction |
|
|
|
| 25.02. |
01 ImageNet: A Large-Scale Hierarchical Image
Database |
F. Hasler |
S. Kellenberger, S. Singh |
D. Thul |
| 25.02. |
02 Playing for Data: Ground Truth from Computer Games
|
B. Walser |
N. Baumann, C. Fennan |
M. Oswald |
| 04.03. |
03 Learning representations by
back-propagating
errors |
M. Mihajlovic |
F. Hasler, S. Kellenberger |
I. Cherabier |
| 04.03. |
04 Backpropagation applied to
handwritten zip code recognition |
S. Singh |
C. Fennan, L. Fernandes |
D. Thul |
| 11.03. |
05
ImageNet
Classification with Deep Convolutional Neural Networks |
N. Baumann |
B. Walser, C. Sprecher |
M. Oswald |
| 11.03. |
06 Going Deeper with Convolutions |
H. Ho |
S. Panighetti, F. Hasler |
D. Thul |
| 18.03. |
07 Deep residual learning for image recognition |
D. Dimitrov |
M. Mihajlovic, L. Steiner |
Z. Cui |
| 18.03. |
08 Xception: Deep Learning with Depthwise Separable
Convolutions
|
C. Sprecher |
M. Vora, S. Panighetti |
Z. Cui |
| 25.03. |
09 Dropout: a simple way to
prevent
neural networks from overfitting |
R. Zenkl |
H. Ho, B. Walser, M. Tom |
Z. Cui |
| 25.03. |
10
Group
Normalization |
M. Vora |
N. Storni, M. Flowers |
Z. Cui |
| 01.04. |
11 Visualizing and Understanding Convolutional Networks
|
S. Huang |
L. Steiner, R. Zenkl |
M. Dusmanu |
| 01.04. |
12 What Uncertainties Do We Need in Bayesian Deep Learning for
Computer Vision? |
R. Suri |
D. Jin, M. Mihajlovic |
Z. Li |
| 15.04. |
13 Human-level control
through
deep re-inforcement learning |
S. Kellenberger |
R. Zenkl, D. Dimitrov |
M. Oswald |
| 15.04. |
14
Conditional
Random Fields as Recurrent Neural Networks |
L. Steiner |
C. Yao, D. Jin |
M. Oswald |
| 29.04. |
15 Mask R-CNN |
N. Storni |
R. Suri, H. Ho, S. Huang |
Z. Cui |
| 29.04. |
22 AtlasNet: A Papier-Mâché Approach to Learning 3D Surface
Generation |
D. Paschalidou |
L. Fernandes, N. Baumann |
S. Lombardi |
| 06.05. |
17
DSAC-differentiable
RANSAC for Camera Localization |
L. Fernandes |
S. Singh, C. Yao |
V. Larsson |
| 06.05. |
18
Deep
Fundamental Matrix Estimation |
M. Tom |
D. Dimitrov, D. Paschalidou |
V. Larsson |
| 13.05. |
19 Unpaired Image-to-Image Translation using Cycle-Consistent
Adversarial Networks |
D. Jin |
M. Flowers, S. Huang |
Z. Cui |
| 13.05. |
20 DeepTAM: Deep Tracking and Mapping |
S. Panighetti |
C. Sprecher, R. Suri |
M. Geppert |
| 27.05. |
23 Working hard to know your neighbor's margins: Local
descriptor learning loss |
M. Flowers |
M. Tom, N. Storni |
M. Dusmanu |
| 27.05. |
24 Dynamic Routing Between Capsules |
C. Yao |
D. Paschalidou, M. Vora |
M. Oswald |
|
