https://telecombcn-dl.github.io/2017-dlcv/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed 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 image captioning.

1. [course site]
Xavier Giro-i-Nieto
xavier.giro@upc.edu
Associate Professor
Universitat Politecnica de Catalunya
Technical University of Catalonia
Welcome
Day 1 Lecture 1
#DLUPC

2. 2
Instructors
Xavier Giro-i-Nieto
• Web: https://imatge.upc.edu/web/people/xavier-giro
Associate Professor at Universitat Politecnica de Catalunya (UPC)
Escola d’Enginyeria de Terrassa
UPC - ESEIAAT

3. 3
Kevin McGuinness
Research Fellow at Dublin City University (DCU)
SFI Funded Starting Investigator
Insight Centre for Data Analytics
http://www.eeng.dcu.ie/~mcguinne/
Instructors

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Amaia Salvador
PhD Candidate
Image Processing Group (GPI), Universitat Politècnica de Catalunya
https://imatge.upc.edu/web/people/amaia-salvador
Instructors

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Elisa Sayrol
Instructors
Associate Professor at Universitat Politècnica de Catalunya (UPC)
Web: https://imatge.upc.edu/web/people/elisa-sayrol

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Èric Arazo
Phd student at Dublin City University (DCU)
Insight Centre for Data Analytics
Instructors

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Ramon Morros
Instructors
Associate Professor at Universitat Politècnica de Catalunya (UPC)
Web: https://imatge.upc.edu/web/people/josep-ramon-morros
Escola d’Enginyeria de Terrassa
UPC - ESEIAAT

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Verónica Vilaplana
Instructors
Associate Professor at Universitat Politècnica de Catalunya (UPC)
Escola d’Enginyeria de Terrassa
UPC - ESEIAAT
• Web: https://imatge.upc.edu/web/people/veronica-vilaplana

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Javier Ruiz Hidalgo
Instructors
Associate Professor at Universitat Politècnica de Catalunya (UPC)
• Web: https://imatge.upc.edu/web/people/javier-ruiz-hidalgo
Escola d’Enginyeria de Terrassa
UPC - ESEIAAT

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Management
Instructor Area
Xavier Giró Coordination
Kevin McGuinness Lectures
Elisa Sayrol Logistics & Evaluation
Xavier Giró Lab
Amaia Salvador Project
Xavier Giró Web and online material

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Guest lectures
Everybody must sign up for the last day here, and promote it !

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Teaching assistants
Eduard
Ramon
Marta
Coll
Fran
Roldan

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Acknowledgments
Eva
Mohedano
Santiago
Pascual
Jose Adrián
Rodríguez
Fonollosa
Marta Ruiz
Costa-jussà
Antonio
Bonafonte
Javier
Hernando
Alan F.
Smeaton
Noel E.
O’Connor
Jordi
Torres
Ferran
Marqués

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Densely linked slides

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Motivation
Source: 50 Best jobs in America (Glassdoor)
The best job in the USA: Data scientist.

16. 16
The Economist, “Million-dollar babies” (02/04/2016)
Motivation

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Nature, “AI talent grab sparks excitement and concern” (26/04/2016)
Motivation

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Annual Conference on Neural Information Processing Systems
(NIPS) @ Barcelona (2016)
Motivation

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Ian Goodfellow and Yoshua Bengio and Aaron Courville, “Deep Learning”. MIT Press 2016.
Motivation

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Motivation
[course site][course site][course site]

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Motivation
[course site]

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Motivation
“NVIDIA to train 100,000 Developers in Deep Learning in 2017.”

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Motivation
Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
The AI Hype
ALGORITHMS
Deep Learning
BIG DATA
Vision: ImageNet
BIG COMPUTATION
GPUs

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Motivation
Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
The AI Hype
ALGORITHMS
Deep Learning
BIG DATA
Vision: ImageNet
BIG COMPUTATION
GPUs

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Motivation
Hubel, David H., and Torsten N. Wiesel. "Receptive fields, binocular interaction and functional architecture in the
cat's visual cortex." The Journal of physiology 160, no. 1 (1962): 106-154.
Hubel, David H., and Torsten N. Wiesel. "Receptive fields and functional architecture of monkey striate cortex."
The Journal of physiology 195, no. 1 (1968): 215-243.
Inspiration from Neuroscience:
Hierarchical model of the visual pathway
with neurons responding to:
● oriented edges and bars @ lower
areas
● specific stimuli @ higher areas

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Motivation
Fukushima, Kunihiko, and Sei Miyake. "Neocognitron: A self-organizing neural network model for a mechanism
of visual pattern recognition unaffected by a shift in position." In Competition and cooperation in neural nets, pp.
267-285. Springer Berlin Heidelberg, 1982.
Hierarchical model applied to a neural
network:
● alternating layers of simple and
complex cells.
● down sampling
● shift invariance (convolutions)

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Motivation
Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. "Learning representations by back-propagating
errors." Cognitive modeling 5, no. 3 (1988).
Training a neural network with
the back-propagation algorithm
(backprop).

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Motivation
LeCun, Yann, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, and
Lawrence D. Jackel. "Backpropagation applied to handwritten zip code recognition." Neural computation 1, no. 4
(1989): 541-551.
Hierarchical neural model
+
backpropagation.

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Motivation
Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9, no.
8 (1997): 1735-1780.

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Motivation
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural
networks." NIPS 2012
12,573 citations
(June 2017)

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Motivation
Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.

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Motivation
Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
The AI Hype
ALGORITHMS
Deep Learning
BIG DATA
Vision: ImageNet
BIG COMPUTATION
GPUs

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Motivation
Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical
image database." CVPR 2009.

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Motivation
Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
The AI Hype
ALGORITHMS
Deep Learning
BIG DATA
Vision: ImageNet
BIG COMPUTATION
GPUs

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Motivation

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Motivation
Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
The AI Hype
ALGORITHMS
Deep Learning
BIG DATA
Vision: ImageNet
BIG COMPUTATION
GPUs

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What’s new this 2017 ?
● From 2 to 5 instructors from UPC Image Processing Group.
● Labs provided by NVIDIA Deep Learning Institute.
● Two GPUs per each team on GCloud.
● Projects based on the “Nuts & Bolts” tutorial by Andrew Ng.
● Public presentations of students projects.
● Two guest speakers: Àgata Lapedriza & Elisenda Bou.
● DLCV 2016 and DLSL 2017 videos are available to review.

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Discussion
https://piazza.com/upc/summer2017/230360/home
Pose your logistic questions (only of interest for course
participants) on this private course management platform:

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Discussion
https://www.reddit.com/r/dlupc/
Pose your technical questions (of broad interest) on this
public subredit (similar to Stanford cs231n’s):

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http://bit.ly/dlcv2017
Course material
First on Piazza, later on
the public site.

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MSc BSc Guests
3:00-
4:00
Lectures @ D5-010
4:00-
5:00
Lectures @ D5-010
5:00-
6:00
Labs @
D5-004
Labs @
D5-005
6:00-
7:00
Project @
D5-004 &
-007
(optional)
Project
Wednesday to Monday
@ D5
Tuesday
@ B3-Sala Teleensenyament (2nd floor)
MSc BSc Guests General
public
3:00-
5:00
Project presentations
5:00-
5:40
Guest Lecture: Àgata Lapedriza
5:40-
6:20
Guest Lecture: Elisenda Bou
6:20-
7-00
Closing
Project
Schedule

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Grading
BSc MSc
Online Tests 60% 30%
Lab 30% 30%
Project - 30%
Communication - 10%
Attendance 10% -10% x miss day
Reddit +10% (if valuable) +10% (if valuable)

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Grading: Online tests
[Online test preview]

45. Questions?