263-5904-00L  Deep Learning for Computer Vision: Seminal Work

SemesterSpring Semester 2022
LecturersI. Armeni
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber 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.



Courses

NumberTitleHoursLecturers
263-5904-00 SDeep Learning for Computer Vision: Seminal Work2 hrs
Mon16:15-18:00CAB G 57 »
I. Armeni

Catalogue data

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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersI. Armeni
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places24 at the most
PriorityRegistration for the course unit is only possible for the primary target group
Primary target groupElectrical Engin. + Information Technology MSc (237000)
Computer Science MSc (263000)
CAS ETH in Computer Science (269000)
Waiting listuntil 06.03.2022

Offered in

ProgrammeSectionType
CAS in Computer ScienceSeminarsWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Computer Science MasterSeminarWInformation
Computer Science MasterSeminar in General StudiesWInformation