Deep Learning for Computer Vision [PDF]

representations and tasks directly from images, text and sound. Traditional Machine Learning. Machine. Learning. Classif

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MASTERCLASS: Deep Learning for Computer Vision with MATLAB

Amine EL HELOU, [email protected] Valerie LEUNG, [email protected]

© 2016 The MathWorks, Inc. 1

Deep Learning is Ubiquitous Computer Vision  Pedestrian and traffic sign detection  Landmark identification  Scene recognition  Medical diagnosis and drug discovery Text and Signal Processing  Speech Recognition  Speech & Text Translation

Robotics & Controls and many more… 2

What is Deep Learning ? Deep learning performs end-end learning by learning features, representations and tasks directly from images, text and sound Traditional Machine Learning

Manual Feature Extraction

Classification Machine Learning

Car  Truck  

Bicycle  Deep Learning approach

Convolutional Neural Network (CNN) Learned features 𝟗𝟓% End-to-end learning … 𝟑%  Feature learning + Classification 𝟐%

Car  Truck  

Bicycle

3

Why is Deep Learning so Popular ? Year 

Results: Achieved substantially better results on ImageNet large scale recognition challenge – 95% + accuracy on ImageNet 1000 class challenge



Computing Power: GPU’s and advances to processor technologies have enabled us to train networks on massive sets of data.



Data: Availability of storage and access to large sets of labeled data – E.g. ImageNet , PASCAL VoC , Kaggle

Pre-2012 (traditional computer vision and machine learning techniques)

Error Rate > 25%

2012 (Deep Learning )

~ 15%

2015 ( Deep Learning)

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