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Block-Seminar on Deep Learning for Bio-Medical Data Analysis

apl. Prof. Olaf Ronneberger (DeepMind)

In this seminar you will learn about relevant bio-medical research fields and the most recent methods (mainly based on deep learning) that have been already applied to bio-medical data, or that have a large potential in this field. Especially generative models and unsupervised methods have a large potential to learn concepts from large non-annotated data bases (see a recent blog post from DeepMind on "Unsupervised learning: the curious pupil"). For each paper there will be one person, who performs a detailed investigation of a research paper and its background and will give a presentation. The presentation is followed by a discussion with all participants about the merits and limitations of the respective paper. You will learn to read and understand contemporary research papers, to give a good oral presentation, to ask questions, and to openly discuss a research problem. The maximum number of students that can participate in the seminar is 12.


Due to the COVID-19 pandemic the seminar will be entirely held online, i.e. the student presentations will be given with a teleconferencing tool using screen sharing, as will be the discussions of the papers.

Blockseminar:
(2 SWS)
T.B.A.
Online via teleconference (unless university reopens)
Contact person: Silvio Galesso

Beginning: Watch the following lectures before April 21:
Giving a good presentation
Proper scientific behavior
(the access credentials are the same as other computer vision courses, if you don't know them get in touch).
If you want to participate register on HisInOne for the course, attend the first meeting on April 21 at 11:00 c.t. and send an email with your name and your paper priorities (B1-B13, favorite paper first) to Silvio Galesso before April 26.
The slides of the introductory meeting are available here.

Mid-Semester Meeting: T.B.A.
Video conference link will be in the e-mails
Introduction to Neural Networks by apl. Prof. Olaf Ronneberger (DeepMind)

ECTS Credits: 4

Recommended semester:

6 (Bachelor), any (Master)
Requirements: Background in computer vision

Remarks: This course is offered to both Bachelor and Master students. The language of this course is English. All presentations must be given in English.

Topics will be assigned via a preference voting (detailed information will follow). Please register for the seminar online before the first meeting. If you could not register still come to our introductory online meeting to see if there are papers free. If there are more interested students than places, places will be assigned by a mixture of motivation in the first meeting and priority suggestions of the system. The date of registration is NOT important. In particular, we want to avoid that people grab a topic and then jump off during the semester. Please have a coarse look at all available papers to make an informed decision before you commit. The listed papers are not yet sorted by the time of presentation.

Important!
Please get in contact with your advisor as soon as possible, and at least 4 weeks before your presentation

Submit your presentation outline to your advisor at least 2 weeks before your presentation and meet with your advisor.

Submit your presentation slides to your advisor at least 1 week before your presentation and meet again.

All participants must read all papers and answer a few questions. The questions will be available here. The answers must be sent to the corresponding advisor until t.b.a.. We highly recommend to read and understand all papers first, before you start to prepare your presentation.


Slides of the introductory lecture
Powerpoint template for your presentation (optional)

Papers:

ID Paper Student   Advisor Slides  
B1 Learning Transferable Visual Models From Natural Language Supervision Johannes Ruf
B2 Zero-Shot Text-to-Image Generation
B3 Generating Images with Sparse Representations
B4 ViViT: A Video Vision Transformer
B5 Characterizing signal propagation to close the performance gap in unnormalized ResNets Öner Aydogan
B6 Perceiver: General Perception with Iterative Attention Victor George
B7 Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures Roya Rahimzadeh
B8 Broaden Your Views for Self-Supervised Video Learning Anna Stroganova
B9 Unsupervised Learning of Dense Visual Representations Kashan Karimudin
B10 Efficient Visual Pretraining with Contrastive Detection Stanley George
B11 Predicting Video with VQVAE Carlos Marañes
B12 NeRF-VAE: A Geometry Aware 3D Scene Generative Model
B13 Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers Muhammad Irfan