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.


  • 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 DT
Playing for Data: Ground Truth from Computer Games IC
ImageNet Classification with Deep Convolutional Neural Networks AC
5.3. Very Deep Convolutional Networks for Large-Scale Image Recognition AC
Going Deeper with Convolutions VT
Deep residual learning for image recognition LK
12.3. Learning representations by back-propagating errors IC
Backpropagation applied to handwritten zip code recognition DT
19.3. Deep sparse rectifier neural networks VT
Multi-Scale Context Aggregation by Dilated Convolutions LK
26.3. Extracting and composing robust features with denoising autoencoders LK
Generative Adversarial Networks VT
9.4. Dropout: a simple way to prevent neural networks from overfitting AC
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift DT
23.4. Rich feature hierarchies for accurate object detection and semantic segmentation ZC
You Only Look Once: Unified, Real-Time Object Detection AC
30.4. Visualizing and Understanding Convolutional Networks LK
Fully convolutional networks for semantic segmentation ZC
7.5. Conditional Random Fields as Recurrent Neural Networks LK
Long-term recurrent convolutional networks for visual recognition and description VT
14.5. Spatial Transformer Networks KT
Dynamic Routing Between Capsules LK
28.5. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network VT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks AC

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