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.
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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
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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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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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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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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
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
37. 37
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.