ETH Zurich - D-INFK - IVC - CVG - Lectures - Deep Learning Seminar

Deep Learning Seminar

Course title: Deep Learning for Computer Vision: Seminal Work
Course ID: 263-5904-00L
Lecturers: Dr. Torsten Sattler, Dr. Lisa Koch
Teaching assistants: Dr. Vagia Tsiminaki, Dr. Andrea Cohen, Ian Cherabier, Daniel Thul, Dr. Zhaopeng Cui, Katarina Tóthová
Venue: Mo 15-17h in CAB G57

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.

The seminar will start with introductory lectures to provide (1) a compact overview of challenges and relevant machine learning and deep learning research, and (2) a tutorial on critical analysis and presentation of research papers. Each student then chooses one paper from the provided collection to present during the remainder of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants.


  • 20 February: All the slides from the first session are online.
  • 19 February: The introductory slides from today's session are online. Rest will follow soon.
  • 2 February: The preliminary schedule and paper selection are online!

Important information

The students will present one paper each. However, they are 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. The 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).


Schedule (Tentative)

Date Topic Presenter Critics Assistant
19.2. Introduction
26.2. ImageNet: A Large-Scale Hierarchical Image Database R. Brügger I. Beloshapka,
S. Curry
D. Thul
Playing for Data: Ground Truth from Computer Games T. Tishchenko S. Häfeli,
V. Vitchevsky
I. Cherabier
ImageNet Classification with Deep Convolutional Neural Networks G. Russo A. Oertel,
M. Gupta
L. Koch
5.3. Very Deep Convolutional Networks for Large-Scale Image Recognition A. Cohen
Going Deeper with Convolutions D. Thul
Deep residual learning for image recognition L. Koch
12.3. Learning representations by back-propagating errors I. Cherabier
Backpropagation applied to handwritten zip code recognition D. Thul
19.3. Deep sparse rectifier neural networks V. Tsiminaki
Multi-Scale Context Aggregation by Dilated Convolutions L. Koch
26.3. Extracting and composing robust features with denoising autoencoders L. Koch
Generative Adversarial Networks V. Tsiminaki
9.4. Dropout: a simple way to prevent neural networks from overfitting A. Cohen
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift V. Tsiminaki
23.4. Rich feature hierarchies for accurate object detection and semantic segmentation Z. Cui
You Only Look Once: Unified, Real-Time Object Detection A. Cohen
30.4. Visualizing and Understanding Convolutional Networks L. Koch
Fully convolutional networks for semantic segmentation Z. Cui
7.5. Conditional Random Fields as Recurrent Neural Networks L. Koch
Long-term recurrent convolutional networks for visual recognition and description V. Tsiminaki
14.5. Spatial Transformer Networks K. Tóthová
Dynamic Routing Between Capsules A. Cohen
28.5. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network V. Tsiminaki
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks A. Cohen

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