The spring semester 2021 will take place online until further notice. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.

Zhaopeng Cui: Catalogue data in Spring Semester 2019

Name Dr. Zhaopeng Cui
Address
Professur für Informatik
ETH Zürich, CNB G 103.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
E-mailzhaopeng.cui@inf.ethz.ch
DepartmentComputer Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
263-5904-00LDeep Learning for Computer Vision: Seminal Work Information Restricted registration - show details
Number of participants limited to 24.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SZ. Cui
AbstractThis 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.
ObjectiveThe 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.
ContentThe 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.
Lecture notesThe selection of research papers will be presented at the beginning of the semester.
LiteratureThe course "Machine Learning" is recommended.