GANs for Exploiting Unlabeled Dataweb.eng.tau.ac.il/deep_learn/wp-content/uploads/... · Learning...
Transcript of GANs for Exploiting Unlabeled Dataweb.eng.tau.ac.il/deep_learn/wp-content/uploads/... · Learning...
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GANs for Exploiting Unlabeled Data
Improved Techniques for Training GANs
Learning from Simulated and Unsupervised Images through Adversarial Training
Presented by:Uriya Pesso
Nimrod Gilboa Markevich
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“[…] you could not see an article in the press [about AI] without the picture being Terminator. It was always Terminator, 100 percent. And you see less of that now, and that’s a good thing “
Yann LeCun, Director, Facebook AI
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“Generative Adversarial Networks is the most interesting idea in the last ten years in machine learning.”Yann LeCun, Director, Facebook AI
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Presentation Overviewo Motivation
o Intro to GANs
o Paper 1: Semi Supervised Learning
o Paper 2: Simulated and Unsupervised Learning
o Conclusion
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Motivation –Compensating for Missing Datao Labeled data is expensive (time, money, effort)
o Unlabeled data is cheaper
o Unlabeled data still contains information
o How can we utilize unlabeled data?
o GANs!◦ We will present two methods from two papers
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Generative Adversarial Networkso What does it do?
◦ Generate synthetic data that is indistinguishable from real data
o Uses◦ Super-Resolution
◦ Text-to-Speech
◦ Art
◦ Exploiting unlabeled data
(for training other networks)
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Generative Adversarial Networkso Adversarial Networks – Opponent Networks
◦ Generator – Create realistic samples
◦ Discriminator – Distinguish generated from real
o Compete with each other
o The only labels are Real/Fake
o Gradient Descent, Train in turns
Train D
Train G
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Loss Functions
o Cross-entropy loss function
o – probability of x being real
o – generated sample from noise z
o – network parameters
o – discriminator loss function
o – Generator loss function
data~ ~noise, log log 1
D D G
pL D D G x zx z
G DL L
D x
G z
,
D G
DL
GL
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Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
Improved Techniques for Training GANs
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Semi-Supervised Learning - Ideao Objective: We want to train a classifier
o We have:◦ labeled data => supervised learning
◦ unlabeled data => unsupervised learning
o Combining labeled and unlabeled data => semi-supervised
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Semi-Supervised Learning - Ideao Combine GAN and classifier networks.
o Add a label for the synthetic data - K+1
1,.., , 1y K K
DClassifier
x or x*
yG(z)G
Generatorz
1,..,y K
Classifierx y
Generated,Realy
DDiscriminator
x*
yG(z)G
Generatorz
Supervised
Unsupervised
Semi-Supervised
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Semi-Supervised Loss Functiono Cross entropy loss over two distributions
data, ~ ( , ) modellog |y p yL p y x x x
datasupervised , ~ ( , ) modellog | , 1p yL p y y K x y x x
dataunsupervised ~ ( ) model ~ modellog 1 1| log 1|p GL p y K p y K x x xx x
◦ - is the probability distribution of the input data.
◦ - is the model probability distribution to predict label y from K labels given data X.
data ,p yx
modellog |p y x
~ modellog 1|G p y K x x supervised unsupervisedL L
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Supervised Loss
o Where:◦ - is the probability distribution of the input data.
◦ - is the model probability distribution to predict label y from K labels given data X.
1,.., , 1y K K
Classifierx y
datap x
datasuprvised , ~ ( , ) modellog | , 1y p yL p y y K x x x
modellog |p y x
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Supervised Loss
o - is the probability distribution of the input data.
o - is the model probability distribution to predict label y from K labels given data X.
1,..,y K
Classifierx y
datap x
datasuprvised , ~ ( , ) modellog |p yL p y x y x x
modellog |p y x
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Unsupervised Loss
o – the probability distribution to predict that the data x is unreal.
dataunsuprvised ~ ( ) model ~ ( ) modellog(1 ( 1| )) log( ( 1| ))p GL p y K p y K x x x zx x
Generated,Realy
DDiscriminator
x
y
G(z)GGenerator
z
data~ ~noise, log log 1
D D G
pL D D G x zx z
model( ) 1 ( 1| )D p y K x x
model ( 1| )p y K x
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Punchline
data
data
supervised unsupervised
supervised , ~ ( , ) model
unsupervised ~ ( ) model ~ model
log | , 1
log 1 1| log 1|
p y
p G
L L L
L p y y K
L p y K p y K
x y x
x x x
x
x x
Classifier
GAN
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Semi-Supervised Learning – Intuitiono How does a classifier benefit from unlabeled data?
o From the unlabeled data, the classifier learns to focus on the right features, thus reducing generalization error
Number Not Number
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Results
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Results - MNIST
Generated Real
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Results - MNIST
(*) number of labeled samples per class the rest are unlabeledtrain size: 60,000test size: 10,000
(*)
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Results – CIFAR10
CIFAR10 Generated
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Results – Imagenet
DCGAN(Not “Ours”)
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Results – Imagenet
“Our”Method
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Avish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ WebbApple Inc
Learning from Simulated and Unsupervised Images through Adversarial Training
CVPR 2017 Best Paper Award
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Motivationo Labeled data is expensive (time, money, effort)
o Simulated data is cheap
o Alternatively, we could learn using simulated data
Simulator
Data Set of
LabeledSyntheticImages
NN
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Motivationo Drawback: Simulated data may have artifacts
o We want more realistic synthetic data
o We have unlabeled data
o Let’s build a refiner
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Refiner Network / S+U Learning
Simulator
Data Set of
LabeledSyntheticImages
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SimGAN Architecture
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SimGAN Architecture
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Loss Function – Discriminatoro Adversarial Loss
real simulated~ ~
, log log 1D D R
p pL D D R
x x x xx x
o – probability of x being real
o – refined simulated image
o – network parameters
o – discriminator loss function
D x
R x
,
D R
DL
o – pdf of real images
o – pdf of simulated images
realp x
simulatedp x
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Loss Function – Refinero Refiner has two goals
◦ Generate realistic samples – Adversarial Loss
◦ Preserve the label – Self-regularization
simulatedreal reg~
,R D R
pL x x
real log D R x reg 1R x x
o – refined simulated image
o – network parameters
o – refiner loss function
R x o – pdf of simulated images simulatedp x
,
D R
RL o – weight
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Minor Improvementso Are we done?
o Almost. There are two additional mechanisms:◦ Local Adversarial Loss
◦ Using a History of Refined Images
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Local Adversarial Loss
o Discriminator outputs wxh probability map
o The adversarial loss is the sum of the loss over the local patches
o Localization restrains artifacts – the refined image should look real in every patch
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Local Adversarial Loss
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Using a History of Refined Imageso Problem
◦ Discriminator only focuses on the latest refined images
◦ Refiner might reintroduce artifacts that the discriminator has forgotten
o Solution◦ Buffer refined images
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Using a History of Refined Images
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Using a History of Refined Images
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Results
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Stages for SimGAN Performance Evaluation
1) Train SimGAN
2) Generate Synthetic Refined Dataset => Qualitative Results
3) Train NN (Estimator) with the Dataset
4) Test NN (Estimator) => Quantitative Results
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Results - Performance Evaluation using Gaze Estimatioro Simulated images – UnityEyes, 1.2M
o Real Labeled Dataset – MPIIGaze Dataset, 214K
Gaze Estimator
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Process – 1. Train SimGAN
UnityEyes
MPIIGaze (without labels)
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Data Set of Labeled Synthetic Images
Process – 2. Trained Refiner Generates DB
SimGANRefiner
Data Set of Labeled RefinedSynthetic Images
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Qualitative Results
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Process – 3. Train Gaze Estimator
Data Set of Labeled SyntheticRefined Images
Gaze Estimator
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Data Set of Real Images
Process – 4. Test Gaze Estimator
Gaze Estimator
CNN
Output
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Quantitative Results
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Results – Performance Evaluation using Hand Pose Estimatioro NYU hand pose dataset, 73,000 training, 8,000 testing
Hand Pose
Estimator
Selected Points CoordinatesDepth Image
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Qualitative Result
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Quantitative Results
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Quantitative Results
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Conclusiono Generative Adversarial Networks are awesome
◦ Generator vs. Discriminator
o Unlabeled data can be used for supervised learning◦ Semi-Supervised Learning
◦ Classifier combined with Discriminator
◦ Train GAN with labeled and unlabeled data
◦ Simulated and Unsupervised Learning◦ Train Refiner
◦ Generate large synthetic refined dataset