Deep Learning & Feature Learning Methods for Vision Rob Fergus (NYU) Kai Yu (Baidu) Marc’Aurelio Ranzato (Google) Honglak Lee (Michigan) Ruslan Salakhutdinov (U. Toronto) Graham Taylor (University of Guelph) CVPR 2012 Tutorial: 9am-5:30pm
Deep Learning & Feature Learning Methods for Vision Rob Fergus (NYU) Kai Yu (Baidu) Marc’Aurelio Ranzato (Google) Honglak Lee (Michigan) Ruslan Salakhutdinov.
Dec 18, 2015
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- Slide 1
- Deep Learning & Feature Learning Methods for Vision Rob Fergus (NYU) Kai Yu (Baidu) MarcAurelio Ranzato (Google) Honglak Lee (Michigan) Ruslan Salakhutdinov (U. Toronto) Graham Taylor (University of Guelph) CVPR 2012 Tutorial: 9am-5:30pm
- Slide 2
- Tutorial Overview 9.00am:IntroductionRob Fergus (NYU) 10.00am:Coffee Break 10.30am:Sparse CodingKai Yu (Baidu) 11.30am:Neural NetworksMarcAurelio Ranzato (Google) 12.30pm:Lunch 1.30pm:Restricted Boltzmann Honglak Lee (Michigan) Machines 2.30pm:Deep BoltzmannRuslan Salakhutdinov (Toronto) Machines 3.00pm:Coffee Break 3.30pm:Transfer Learning Ruslan Salakhutdinov (Toronto) 4.00pm:Motion & Video Graham Taylor (Guelph) 5.00pm:Summary / Q & AAll 5.30pm:End
- Slide 3
- Overview Learning Feature Hierarchies for Vision Mainly for recognition Many possible titles: Deep Learning Feature Learning Unsupervised Feature Learning This talk: Basic concepts Links to existing vision approaches
- Slide 4
- Existing Recognition Approach Hand- designed Feature Extraction Trainable Classifier Image/Video Pixels Features are not learned Trainable classifier is often generic (e.g. SVM) Object Class Slide: Y.LeCun
- Slide 5
- Motivation Features are key to recent progress in recognition Multitude of hand-designed features currently in use SIFT, HOG, LBP, MSER, Color-SIFT. Where next? Better classifiers? Or keep building more features? Felzenszwalb, Girshick, McAllester and Ramanan, PAMI 2007 Yan & Huang (Winner of PASCAL 2010 classification competition)
- Slide 6
- What Limits Current Performance? Ablation studies on Deformable Parts Model Felzenszwalb, Girshick, McAllester, Ramanan, PAMI10 Replace each part with humans (Amazon Turk) : Also removal of part deformations has small (
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