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deeplearning-annotated.pdf
1.
Machine Learning Specialization Deep
Learning: Searching for Images Emily Fox & Carlos Guestrin Machine Learning Specialization University of Washington ©2015 Emily Fox & Carlos Guestrin
2.
Machine Learning Specialization Visual
product recommender ©2015 Emily Fox & Carlos Guestrin
3.
Machine Learning Specialization 3
I want to buy new shoes, but… ©2015 Emily Fox & Carlos Guestrin Too many options online…
4.
Machine Learning Specialization 4
Text search doesn’t help… ©2015 Emily Fox & Carlos Guestrin “Dress shoes”
5.
Machine Learning Specialization Visual
product search demo ©2015 Emily Fox & Carlos Guestrin
6.
Machine Learning Specialization Features
are key to machine learning ©2015 Emily Fox & Carlos Guestrin
7.
Machine Learning Specialization 7
Goal: revisit classifiers, but using more complex, non-linear features ©2015 Emily Fox & Carlos Guestrin Sentence from review Classifier MODEL Input: x Output: y Predicted class
8.
Machine Learning Specialization 8
Image classification ©2015 Emily Fox & Carlos Guestrin Input: x Image pixels Output: y Predicted object
9.
Machine Learning Specialization Neural
networks ê Learning *very* non-linear features ©2015 Emily Fox & Carlos Guestrin
10.
Machine Learning Specialization 10
Linear classifiers w 0 + w 1 x 1 + w 2 x 2 + … + w d x d = 0 Score(x) > 0 Score(x) < 0 ©2015 Emily Fox & Carlos Guestrin Score(x) = w0 + w1 x1 + w2 x2 + … + wd xd
11.
Machine Learning Specialization 11
Graph representation of classifier: useful for defining neural networks x1 x2 xd y … 1 w1 w2 > 0, output 1 < 0, output 0 Input Output ©2015 Emily Fox & Carlos Guestrin Score(x) = w0 + w1 x1 + w2 x2 + … + wd xd
12.
Machine Learning Specialization 12
What can a linear classifier represent? x1 OR x2 x1 AND x2 x1 x2 1 y x1 x2 1 y ©2015 Emily Fox & Carlos Guestrin
13.
Machine Learning Specialization 13
What can’t a simple linear classifier represent? XOR the counterexample to everything Need non-linear features ©2015 Emily Fox & Carlos Guestrin
14.
Machine Learning Specialization Solving
the XOR problem: Adding a layer XOR = x1 AND NOT x2 OR NOT x1 AND x2 z1 -0.5 1 -1 z1 z2 z2 -0.5 -1 1 x1 x2 1 y 1 -0.5 1 1 Thresholded to 0 or 1 ©2015 Emily Fox & Carlos Guestrin
15.
Machine Learning Specialization 15
A neural network • Layers and layers and layers of linear models and non-linear transformations • Around for about 50 years - Fell in “disfavor” in 90s • In last few years, big resurgence - Impressive accuracy on several benchmark problems - Powered by huge datasets, GPUs, & modeling/learning alg improvements x1 x2 1 z1 z2 1 y ©2015 Emily Fox & Carlos Guestrin
16.
Machine Learning Specialization Application
of deep learning to computer vision ©2015 Emily Fox & Carlos Guestrin
17.
Machine Learning Specialization 17
Image features • Features = local detectors - Combined to make prediction - (in reality, features are more low-level) Face! Eye Eye Nose Mouth ©2015 Emily Fox & Carlos Guestrin
18.
Machine Learning Specialization 18
Typical local detectors look for locally “interesting points” in image • Image features: collections of locally interesting points - Combined to build classifiers ©2015 Emily Fox & Carlos Guestrin Face!
19.
Machine Learning Specialization 19
SIFT [Lowe ‘99] ©2015 Emily Fox & Carlos Guestrin •Spin Images [Johnson & Herbert ‘99] •Textons [Malik et al. ‘99] •RIFT [Lazebnik ’04] •GLOH [Mikolajczyk & Schmid ‘05] •HoG [Dalal & Triggs ‘05] •… Many hand created features exist for finding interest points…
20.
Machine Learning Specialization 20
Standard image classification approach Input Use simple classifier e.g., logistic regression, SVMs Face? ©2015 Emily Fox & Carlos Guestrin Extract features Hand-created features
21.
Machine Learning Specialization 21
SIFT [Lowe ‘99] ©2015 Emily Fox & Carlos Guestrin •Spin Images [Johnson & Herbert ‘99] •Textons [Malik et al. ‘99] •RIFT [Lazebnik ’04] •GLOH [Mikolajczyk & Schmid ‘05] •HoG [Dalal & Triggs ‘05] •… Many hand created features exist for finding interest points… Hand-created features … but very painful to design
22.
Machine Learning Specialization 22
Deep learning: implicitly learns features Layer 1 Layer 2 Layer 3 Prediction ©2015 Emily Fox & Carlos Guestrin Example detectors learned Example interest points detected [Zeiler & Fergus ‘13]
23.
Machine Learning Specialization Deep
learning performance ©2015 Emily Fox & Carlos Guestrin
24.
Machine Learning Specialization Sample
results using deep neural networks • German traffic sign recognition benchmark - 99.5% accuracy (IDSIA team) • House number recognition - 97.8% accuracy per character [Goodfellow et al. ’13] ©2015 Emily Fox & Carlos Guestrin
25.
Machine Learning Specialization ImageNet
2012 competition: 1.2M training images, 1000 categories ©2015 Emily Fox & Carlos Guestrin 0 0.05 0.1 0.15 0.2 0.25 0.3 SuperVision ISI OXFORD_VGG Error (best of 5 guesses) Huge gain Exploited hand-coded features like SIFT Top 3 teams
26.
Machine Learning Specialization ImageNet
2012 competition: 1.2M training images, 1000 categories ©2015 Emily Fox & Carlos Guestrin Winning entry: SuperVision 8 layers, 60M parameters [Krizhevsky et al. ’12] Achieving these amazing results required: • New learning algorithms • GPU implementation
27.
Machine Learning Specialization Deep
learning in computer vision ©2015 Emily Fox & Carlos Guestrin
28.
Machine Learning Specialization Scene
parsing with deep learning ©2015 Emily Fox & Carlos Guestrin [Farabet et al. ‘13]
29.
Machine Learning Specialization Retrieving
similar images ©2015 Emily Fox & Carlos Guestrin Input Image Nearest neighbors
30.
Machine Learning Specialization Challenges
of deep learning ©2015 Emily Fox & Carlos Guestrin
31.
Machine Learning Specialization Deep
learning score card Pros • Enables learning of features rather than hand tuning • Impressive performance gains - Computer vision - Speech recognition - Some text analysis • Potential for more impact ©2015 Emily Fox & Carlos Guestrin
32.
Machine Learning Specialization Deep
learning workflow Lots of labeled data Training set Validation set Learn deep neural net Validate Adjust parameters, network architecture,… ©2015 Emily Fox & Carlos Guestrin
33.
Machine Learning Specialization 33
Many tricks needed to work well… Different types of layers, connections,… needed for high accuracy [Krizhevsky et al. ’12] ©2015 Emily Fox & Carlos Guestrin
34.
Machine Learning Specialization Deep
learning score card Pros • Enables learning of features rather than hand tuning • Impressive performance gains - Computer vision - Speech recognition - Some text analysis • Potential for more impact Cons • Requires a lot of data for high accuracy • Computationally really expensive • Extremely hard to tune - Choice of architecture - Parameter types - Hyperparameters - Learning algorithm - … ©2015 Emily Fox & Carlos Guestrin Computational cost+ so many choices = incredibly hard to tune
35.
Machine Learning Specialization Deep
features: Deep learning + Transfer learning ©2015 Emily Fox & Carlos Guestrin
36.
Machine Learning Specialization 36
Standard image classification approach Input Use simple classifier e.g., logistic regression, SVMs Face? ©2015 Emily Fox & Carlos Guestrin Extract features Hand-created features Can we learn features from data, even when we don’t have data or time?
37.
Machine Learning Specialization 37
Transfer learning: Use data from one task to help learn on another Lots of data: Learn neural net Great accuracy on cat v. dog Some data: Neural net as feature extractor + Simple classifier Great accuracy on 101 categories Old idea, explored for deep learning by Donahue et al. ’14 & others ©2015 Emily Fox & Carlos Guestrin vs.
38.
Machine Learning Specialization 38
What’s learned in a neural net Very specific to Task 1 Should be ignored for other tasks More generic Can be used as feature extractor ©2015 Emily Fox & Carlos Guestrin vs. Neural net trained for Task 1: cat vs. dog
39.
Machine Learning Specialization 39
Transfer learning in more detail… Very specific to Task 1 Should be ignored for other tasks More generic Can be used as feature extractor ©2015 Emily Fox & Carlos Guestrin For Task 2, predicting 101 categories, learn only end part of neural net Use simple classifier e.g., logistic regression, SVMs, nearest neighbor,… Class? Keep weights fixed! Neural net trained for Task 1: cat vs. dog
40.
Machine Learning Specialization 40
Careful where you cut: latter layers may be too task specific Layer 1 Layer 2 Layer 3 Prediction ©2015 Emily Fox & Carlos Guestrin Example detectors learned Example interest points detected [Zeiler & Fergus ‘13] Too specific for new task Use these!
41.
Machine Learning Specialization Transfer
learning with deep features workflow Some labeled data ©2015 Emily Fox & Carlos Guestrin Extract features with neural net trained on different task Learn simple classifier Validate Training set Validation set
42.
Machine Learning Specialization How
general are deep features? ©2015 Emily Fox & Carlos Guestrin
43.
Machine Learning Specialization Summary
of deep learning ©2015 Emily Fox & Carlos Guestrin
44.
Machine Learning Specialization 44
What you can do now… • Describe multi-layer neural network models • Interpret the role of features as local detectors in computer vision • Relate neural networks to hand-crafted image features • Describe some settings where deep learning achieves significant performance boosts • State the pros & cons of deep learning model • Apply the notion of transfer learning • Use neural network models trained in one domain as features for building a model in another domain • Build an image retrieval tool using deep features ©2015 Emily Fox & Carlos Guestrin
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