227-0085-11L Projects & Seminars: Deep Learning for Image Manipulation (DLIM)
| Semester | Spring Semester 2022 |
| Lecturers | L. Van Gool |
| Periodicity | every semester recurring course |
| Course | Does not take place this semester. |
| Language of instruction | English |
| Comment | Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. |
Courses
| Number | Title | Hours | Lecturers | |
|---|---|---|---|---|
| 227-0085-11 P | Projekte & Seminare: Deep Learning for Image Manipulation (DLIM)
 Does not take place this semester. Für den Zugang zum Angebot und zur Einschreibung loggen Sie sich hier ein (mit Ihrem n.ETHZ account): https://psapp.ee.ethz.ch/ Bitte beachten Sie, dass die Seite jeweils erst zwei Wochen vor Semesterbeginn zugänglich ist und im Verlauf des Semesters wieder abgeschaltet wird. Die Einschreibung ist nur von Freitag vor Semesterbeginn bis zum ersten Freitagmittag im Semester möglich. To access the offer and to enroll for courses log in (with your n.ethz account): https://psapp.ee.ethz.ch/ Please note that the P&S-site is accessible no earlier than two weeks before the start of the semester until four weeks after the start of the semester. Enrollment is only possible from Friday before the start of the semester until noon of the first Friday in the semester. | 3 hrs | L. Van Gool |
Catalogue data
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. |
| Learning objective | Deep Learning – Image Manipulation – Image Enhancement – Image Restoration – Style Transfer – Image to Image Translation – Generative Models – TensorFlow/PyTorch – Projects With the advent of deep learning tremendous advances were achieved in numerous areas from computer vision, computer graphics, and image processing. Using these techniques, an image can be automatically manipulated in various ways with high-quality results, often fooling the human observer. Deep learning based image processing and manipulation are being applied in a vast number of emerging technologies, including image enhancement in smartphone cameras, automated image editing, image content creation, graphics, and autonomous driving. This course focuses on the fundamentals of deep learning and image manipulation. Students will learn the tools to implement and develop deep learning solutions for a variety of image manipulation tasks. The course will end with a 4 weeks project where the students can target a specific application scenario. The course will be taught in English. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 3 credits |
| Examiners | L. Van Gool |
| Type | ungraded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| General |  : Special students and auditors need a special permission from the lecturers |
| Places | Limited number of places. Special selection procedure. |
| Beginning of registration period | Registration possible from 18.02.2022 |
| Priority | Registration for the course unit is only possible for the primary target group |
| Primary target group | Electrical Engin. + Information Technology BSc (228000) |
| Waiting list | until 11.03.2022 |
| End of registration period | Registration only possible until 04.03.2022 |
Offered in
| Programme | Section | Type | |
|---|---|---|---|
| Electrical Engineering and Information Technology Bachelor | Projects & Seminars | W |  |