Deep Learning for Computer Vision Barcelona

Summer seminar UPC TelecomBCN (July 4-8, 2016)

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.

Course Instructors

Teaching assistants

Organizers

Lecture Slides and Videos

Topic Speaker Slideshare YouTube D1L1 Welcome XG Slides D1L2 Classification EM Slides D1L3 Deep networks ES Slides D1L4 Backward Propagation ES Slides Video D1L5 Training EM Slides D1L6 Software Frameworks KM Slides Video D2L1 Memory & Computation KM Slides Video D2L2 Data Augmentation EM Slides D2L3 Visualization AS Slides Video D2L4 Imagenet Challenge XG Slides D2L5 Transfer & Adaptation KM Slides Video D2L6 Recurrent Networks XG Slides Video D3L1 Unsupervised Learning KM Slides Video D3L2 Saliency Prediction ES Slides D3L3 Optimization KM Slides D3L4 Object Detection AS Slides Video D3L5 Face Recognition ES Slides Video D3L6 Image retrieval EM Slides Video D4L1 Generative Models KM Slides D4L2 Segmentation AS Slides Video D4L3 Language and Vision XG Slides Video D4L4 Video Analytics XG Slides Video D4L5 Medical Imaging ES Slides Video D4L6 Attention Models AS Slides Video D5L Closing XG Slides

Hands on TensorFlow

The seminar includes five practical sessions on TensorFlow, the Open Source Software Library for Machine Intelligence developed by Google. These sessions were taught by Professor Jordi Torres, with the teaching assistance of Maurici Yagües. Both of them are part of the Barcelona Supercomputing Center (BSC).

Topic D1T Linear regressor Slides D2T Clustering Slides D3T Neuron & Tensorboard Slides D4T CNN & SLIM Slides D5T RNN Slides

The full course with code snippets is available in this repo.

Student projects

Master students together with some bachelor students organized in teams of five members who solved four directed tasks and developed an open project. The duration of the project corresponds to the single week of the course. Their slides and source code is available from their repos. If you are interested in hiring or contacting the students, some of them have provided their LinkedIn profiles from their project pages.

Team Project Page Slides Repo Team 1 Character autorotation + Autoencoders Web Slides Repo Team 2 Neural Style - Slides Repo Team 3 Generative Adversarial Network - Slides Repo Team 4 Multi-layer Neural Style - Slides Repo Team 5 Deep Dream - Slides Repo

Schedule

When Monday 4 Tuesday 5 Wednesday 6 Thursday 7 Friday 8 3:00-3:20 Welcome Memory Unsupervised Adversarial Project Expo 3 3:20-3:40 Classification Augmentation Saliency Segmentation Project Expo 4 3:40-4:00 Deep Visualization Optimization Language Project Expo 5 4:00-5:00 TensorFlow TensorFlow TensorFlow TensorFlow TensorFlow 4:00-5:00 Project Project Project Project Closing 3,4,5 5:00-5:20 Backpropagation ImageNet Objects Video Project Expo 1 5:20-5:40 Training Transfer Faces Medical Project Expo 2 5:40-6:00 Frameworks Recurrent Ranking Attention Break 6:00-7:00 Project Project Project Project Closing 1,2 6:00-7:00 TensorFlow TensorFlow TensorFlow JT TensorFlow JT TensorFlow

Practical

Course code: 230360 (Phd & master) / 230324 (Bachelor)

ECTS credits: 2.5 (Phd & master) / 2 (bachelor) (corresponds to full-time dedication during the week course)

during the week course) Teaching language: English

The course is offered for both master and bachelor students, but under two study programmes adapted to each profile.

Class Dates: 4-8 July, 2016

Class Schedule: 3-7pm (you will need 6 extra hours a day for homework during the week course)

Capacity: 14 MSc students + 16 BSc students

Location: Campus Nord UPC, Module D5, Room 010

Registration

Registration is sold out for this edition of the seminar. The 30 available seats were covered by UPC TelecomBCN students.

We greatly appreciate the interest of several other students who could not register. We are planning a new edition of this seminar for June-July 2017. A new seminar on Deep Learning for Speech and Language is also planned for January 2017.

You are also encouraged to share your questions and solution in the public issues section for future reference and quality improvement of the course.

Video recordings

Sessions will be recorded in video and posted afterwards, together with the slides.

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find us at the class page.

Related courses

Acknowledgements