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Seminar: Current Trends in Deep Learning (IN2107, IN4515)

SS 2018, TU München

Seminar

Location: Room 02.09.023
Preliminary Meeting: 26.01.2018, 14:00 (please bring a device to access the internet)
Date: Thursday
Time: 14:00 - 16:00
Lecturer: Caner Hazirbas, Philip Haeusser
ECTS: 4
SWS: 2

The course will be held in English. Assignment to this course will be done in the matching system.

We will not favor/rank students who did not participate in the preliminary meeting.

Send your presentation via email to both advisors until 10.07.2018@23:59.

Description

Deep Learning studies computational methods that find patterns in structured data. In this seminar we will discuss current trends in deep learning based on research articles. Every student picks a recent research paper on deep learning for computer vision that she/he presents in the seminar.

Attendance is limited to 14 students (Master).
Registration: Matching system

Important Dates
Preliminary Meeting 26.01.18, 2-4pm Room 02.09.023 Please bring a device to access the internet.
First Lecture 12.04.18, 2-4pm Room 02.09.23 Intro
Second Lecture 19.04.18, 2-4pm Room 02.09.23 Paper assignment
Presentation 10.07.18, 23:59pm email Presentation submission
Preset. Day 1 11.07.18, 10:15am-6pm Room 02.09.23
Preset. Day 2 12.07.18, 10:15am-6pm Room 00.09.38
Final Report 22.07.18, 23:59pm email Report Submission


Useful Documents

Preliminary meeting presentation Lecture lecture

Paper preference form

A LaTeX-template for your seminar report latex template

Paper Assigments
StudentSupervisorPaperPresentation date
Felix MeissenPhilipMultimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer12.07.18
Alexander ZillerCanerDeformable Convolutional Networks11.07.18
Marius ObertPhilipAttend in groups: a weakly-supervised deep learning framework for learning from web data12.07.18
Cem Yusuf AydogduCanerSSD: Single Shot MultiBox Detector11.07.18
Ayşe Hande KaratayPhilipLook, Listen and Learn11.07.18
Felipe PeterCanerOn-Demand Learning for Deep Image Restoration11.07.18
Yang AnPhilipDensePose: Dense Human Pose Estimation In The Wild11.07.18
Nail IbrahimliCanerUnsupervised Learning of Depth and Ego-Motion from Video12.07.18
Marvin AllesPhilipTowards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach12.07.18 - 11.07.18
Maximilian SchneiderCanerLearning to Track at 100 FPS with Deep Regression Networks11.07.18
Moritz KrügenerPhilipFast Face-swap Using Convolutional Neural Networks
Alexander GaulCanerTemporal Convolutional Networks for Action Segmentation and Detection12.07.18
Furkan Mert AlganPhilipAmbient sound provides supervision for visual learning11.07.18
Yehya AbouelnagaCanerAction-Decision Networks for Visual Tracking with Deep Reinforcement Learning12.07.18
Grading Rubric

We are going to grade you based on the following criteria:

Presentation (2/3)

  • presentation has an accessible introduction
  • presentation gets across the key idea of the paper
  • presentation style: less text on slides, more figures
  • presentation style: free but concise
  • presentation: ability to answer questions
  • active participation in discussion of other presentations

Report (1/3)

  • report is well structured
  • report reflects paper completely and correctly
  • report is written in own words, no paraphrasing
  • report contains secondary material
  • report is spell-checked and written in proper English

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85748 Garching info@vision.in.tum.de

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