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Generative Adversarial Networks ( GAN )
The coolest idea in ML in the last twenty years - Yann Lecun
2017.03 Namju Kim (namju.kim@kakaobrainl.com)
Introduction
Taxonomy of ML
Introduction
From David silver, Reinforcement learning (UCL course on RL, 2015).
Supervised Learning
• Find deterministic function f : y = f(x), x : data, y : label
Introduction
Catf F18f
Supervised Learning
• How to implement the following function prototype.
Introduction
Supervised Learning
• Challenges
– Image is high dimensional data
• 224 x 224 x 3 = 150,528
– Many variations
• viewpoint, illumination, deformation, occlusion,
background clutter, intraclass variation
Introduction
Supervised Learning
• Solution
– Feature Vector
• 150,528 → 2,048
– Synonyms
• Latent Vector, Hidden Vector, Unobservable Vector,
Feature, Representation
Introduction
Supervised Learning
Introduction
Supervised Learning
• More flexible solution
– Get probability of the label for given data instead of label
itself
Introduction
Cat : 0.98
Cake : 0.02
Dog : 0.00
f
Supervised Learning
Introduction
Supervised Learning
• Mathematical notation of optimization
– y : label, x : data, z : latent, : learnable parameter
Introduction
given parameterized byprobabilityget when P is maximum
Optimal
Supervised Learning
• Mathematical notation of classifying ( greedy policy )
– y : label, x : data, z : latent, : fixed optimal parameter
Introduction
given parameterized byprobabilityget y when P is maximum
Optimal
label
prediction
Unsupervised Learning
• Find deterministic function f : z = f(x), x : data, z : latent
Introduction
[0.1, 0.3, -0.8, 0.4, … ]f
Good features
• Less redundancy
• Similar features for similar data
• High fidelity
From Yann Lecun, Predictive Learning (NIPS tutorial, 2016).
RL ( cherry )
SL ( icing )
UL ( cake )
Good Bad
Introduction
Unsupervised Learning
Introduction
From Ian Goodfellow, Deep Learning (MIT press, 2016).
E2E
(end-to-end)
Unsupervised Learning
• More challenging than supervised learning :
– No label or curriculum → self learning
• Some NN solutions :
– Boltzmann machine
– Auto-encoder or Variational Inference
– Generative Adversarial Network
Introduction
Generative Model
• Find generation function g : x = g(z), x : data, z : latent
Introduction
[0.1, 0.3, -0.8, 0.4, … ]
g
Unsupervised learning vs. Generative model
• z = f(x) vs. x = g(z)
• P(z|x) vs. P(x|z)
• Encoder vs. Decoder ( Generator )
– P(x, z) needed. ( cf : P(y|x) in supervised learning )
• P(z|x) = P(x, z) / P(x) → P(x) is intractable ( ELBO )
• P(x|z) = P(x, z) / P(z) → P(z) is given. ( prior )
Introduction
Autoencoders
Stacked autoencoder - SAE
• Use data itself as label → Convert UL into reconstruction SL
• z = f(x), x=g(z) → x = g(f(x))
• https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_sae.py
Autoencoders
f
(enc)
g
(dec)
x
x
’z
Supervised learning
with L2 loss ( = MSE )
Denoising autoencoder - DAE
• Almost same as SAE
• Add random noise to input data → Convert UL into denoising SL
• https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_dae.py
Autoencoders
f
(enc)
g
(dec)
x + noise
x
’z
Supervised learning
with L2 loss ( = MSE )
Variational autoencoder - VAE
• Kingma et al, “Auto-Encoding Variational Bayes”, 2013.
• Generative Model + Stacked Autoencoder
– Based on Variational approximation
– other approaches :
• Point estimation ( MAP )
• Markov Chain Monte Carlo ( MCMC )
Autoencoders
Variational autoencoder - VAE
• Training
• https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_vae.py
Autoencoders
f
(enc)
g
(dec)
x
x
’
z :
Supervised learning
with L2 loss ( = MSE ) +
KL regularizer
z+
Variational autoencoder - VAE
• Generating
• https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_vae_eval.py
Autoencoders
g
(dec)
z :
Variational autoencoder - VAE
• Question :
• Solution :
– p(z|x) is intractable but MCMC is slow.
– Introduce auxiliary variational function q(z|x)
– Approximate q(z|x) to p(z|x) by ELBO
– Make q(z|x)
Autoencoders
z = z → p(z|x) =+
Variational autoencoder - VAE
• Simply : → 0, → 1
• KLD Regularizer :
–
– Gaussian case :
• -
• If we fix = 1, ( Practical approach )
– → MSE ( )
Autoencoders
Variational autoencoder - VAE
• KLD ( Kullback-Leibler divergence )
• A sort of distribution difference measure
– cf : KS (Kolmogorov-smirov) test, JSD(Jensen-shannon Divergence)
Autoencoders
Variational autoencoder - VAE
• Find problems in the following code
Autoencoders
Variational autoencoder - VAE
• Reparameterization trick
– Enable back propagation
– Reduce variances of gradients
Autoencoders
f
(enc)
g
(dec)
z :
z+f
(enc)
g
(dec)
z :
Variational autoencoder - VAE
• This is called ‘Reparameterization trick’
Autoencoders
Variational autoencoder - VAE
• Results
Autoencoders
Generative Adversarial Networks
Generative Adversarial Networks - GAN
• Ian Goodfellow et al, “Generative Adversarial Networks”, 2014.
– Learnable cost function
– Mini-Max game based on Nash Equilibrium
• Little assumption
• High fidelity
– Hard to training - no guarantee to equilibrium.
GAN
Generative Adversarial Networks - GAN
• Alternate training
• https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_gan.py
GAN
G
(generator)
z :
D
(discriminator)
real image
fake image
1(Real)
0(fake)
1(real)
Discriminator training
Generator training
Generative Adversarial Networks - GAN
• Generating
• https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_gan_eval.py
GAN
G
(generator)
z :
Generative Adversarial Networks - GAN
• Mathematical notation
GAN
x is sampled
from real data
z is sampled
from N(0, I)
Expectation prob. of D(real) prob. of D(fake)
Maximize DMinimize G
Value of
fake
Generative Adversarial Networks - GAN
• Mathematical notation - discriminator
GAN
Maximize prob. of D(real) Minimize prob. of D(fake)
BCE(binary cross entropy) with label 1 for real, 0 for fake.
( Practically, CE will be OK. or more plausible. )
Generative Adversarial Networks - GAN
• Mathematical notation - generator
GAN
Maximize prob. of D(fake)
BCE(binary cross entropy) with label 1 for fake.
( Practically, CE will be OK. or more plausible. )
Generative Adversarial Networks - GAN
• Mathematical notation - equilibrium
GAN
Jansen-Shannon divergence
0.5
Generative Adversarial Networks - GAN
GAN
Generative Adversarial Networks - GAN
• Result
GAN
Generative Adversarial Networks - GAN
• Why blurry and why sharper ?
GAN
From Ian Goodfellow, Deep Learning (MIT press, 2016) From Christian et al, Photo-realistic single image super-resolution using
generative adversarial networks, 2016
Training GANs
Training GANs
Pitfall of GAN
• No guarantee to equilibrium
– Mode collapsing
– Oscillation
– No indicator when to finish
• All generative model
– Evaluation metrics ( Human turing test )
– Overfitting test ( Nearest Neighbor ) → GAN is robust !!!
– Diversity test
– Wu et al, On the quantitative analysis of decoder-based generative model, 2016
DCGAN
• Radford et al, Unsupervised Representation Learning with Deep
Convolutional Generative Adversarial Networks, 2015
– Tricks for gradient flow
• Max pooling → Strided convolution or average pooling
• Use LeakyReLU instead of ReLU
– Other tricks
• Use batch normal both generator and discriminator
• Use Adam optimizer ( lr = 0.0002, a = 0.9, b=0.5 )
Training GANs
DCGAN
• Results
Training GAN
DCGAN
• Latent vector arithmetic
Training GAN
Pitfall of GAN
Training GANs
mode collapsing oscillating
Escaping mode collapsing
• Minibatch discrimination ( Salimans et al, 2016 ) ← slow
• Replay memory ( Shrivastava et al, 2016 ) ← controversial
• Pull-away term regularizer ( Zhao et al, 2016 ) ← not so good
• Unrolled GAN ( Metz et al, 2016 ) ← not so good
Training GANs
Escaping mode collapsing
• Discriminator ensemble ( Durugkar, 2016) ← not tested
• Latent-Image pair discrimination ( Donahue et al, 2016,
Dumoulin et al, 2016 ) ← Good ( additional computing time )
• InfoGAN ( Chen et al, 2016 ) ← Good
Training GANs
Escaping mode collapsing
Training GANs
Other tricks
• Salimans et al, Improved Techniques for Training GANs, 2016
• https://github.com/soumith/ganhacks
– One-sided label smoothing ← not sure
– Historical averaging ← Unrolled GAN is better
– Feature matching ← working
– Use noise or dropout into real/fake image ← working
– Don’t balance D and G loss ← I agree
Training GANs
Batch normalization in GAN
• Use in G but cautious in D
• Do not use at last layer of G
and first layer of D
• IMHO, don’t use BN in D
• Reference BN or Virtual BN
( Not sure )
Training GANs
Variants of GANs
Taxonomy of Generative Models
Variants of GANs
From Ian Goodfellow, NIPS 2016 Tutorial : Generative
Adversarial Netorks, 2016
SOTA !!!
AR based model :
PixelRNN
WaveNet
Variants of GANs
From Ordena et al, Conditional Image Synthesis With
Auxiliary Classifier GANs, 2016
SOTA with LabelSOTA without Label
Taxonomy of GANs
AFL, ALI
• Donahue et al, Adversarial Feature Learning, 2016
• Domoulin et al, Adversarial Learned Inference, 2016
Variants of GANs
AFL, ALI
• Results
Variants of GANs
InfoGAN
• Chen et al, InfoGAN : Interpretable Representation Learning by
Information Maximizing Generative Adversarial Nets, 2016
Variants of GANs
InfoGAN
• Results
• My implementation : https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_info_gan.py
Variants of GANs
EBGAN
• Junbo et al, Energy-based generative adversarial networks,
2016
Variants of GANs
EBGAN
• Results
Variants of GANs
EBGAN
• My implementation : https://github.com/buriburisuri/ebgan
Variants of GANs
ACGAN
• Ordena et al, Conditional Image Synthesis with Auxiliary
Classifier GANs, 2016
Variants of GANs
ACGAN
• Results
Variants of GANs
ACGAN
• My implementation : https://github.com/buriburisuri/ac-gan
Variants of GANs
WGAN
• Martin et al, Wasserstein GAN, 2017
Variants of GANs
G
(generator)
z :
C
(critic)
real image
fake image
High value
Low value
High value
Critic training
Generator training
Weight Clamping
( No BCE )
WGAN
• Martin et al, Wasserstein GAN, 2017
– JSD → Earth Mover Distance(=Wasserstein-1 distance)
– Prevent the gradient vanishing by using weak distance metrics
– Provide the parsimonious training indicator.
Variants of GANs
WGAN
• Results
Variants of GANs
BGAN
• R Devon et al, Boundary-Seeking GAN, 2017
– Importance Sampling + Boundary Seeking
– Discrete GAN training possible
– Stabilized continuous GAN training
Variants of GANs
Application of GANs
Generating image
• Wang et al, Generative Image Modeling using Style and
Structure Adversarial Networks, 2016
Application of GANs
Generating image
• Wang et al : Results
Application of GANs
Generating image
• Jamonglab, 128x96 face image generation, 2016.11
Application of GANs
Generating image
• Two-step generation : Sketch → Color
• Binomial random seed instead of gaussian
Application of GANs
Generating image
• Zhang et al, StackGAN : Text to Photo-realistic Image Synthesis
with Stacked GANs, 2016
Application of GANs
Generating image
• Zhang et al : Results
Application of GANs
Generating image
• Reed et al, Learning What and Where to Draw, 2016
Application of GANs
Generating image
• Reed et al : Results
Application of GANs
Generating image
• Nguyen et al, Plug & Play Generative Networks, 2016
– Complex langevin sampling via DAE
Application of GANs
Generating image
• Arici et al, Associative Adversarial Networks, 2016
–
Application of GANs
p(z|x) =
Generating image
• Arici et al : Results
Application of GANs
Translating image
• Perarnau et al, Invertible Conditional GANs for image editing,
2016
Application of GANs
Translating image
• Perarnau et al : results
Application of GANs
Translating image
• Isola et al, Image-to-image translation with convolutional
adversarial networks, 2016
Application of GANs
Translating image
• Isola et al : Results
Application of GANs
Translating image
• Ledig et al, Photo-Realistic Single Image Super-Resolution
Using a GAN, 2016
Application of GANs
Translating image
• Ledig et al : Results
Application of GANs
Translating image
• Sajjadi et al, EnhanceNet : Single Image Super-Resolution
through Automated Texture Synthesis, 2016
– SRGAN + Gram metrics loss
Application of GANs
Domain adaptation
• Taigman et al, Unsupervised cross-domain image generation,
2016
Application of GANs
Domain adaptation
• Taigman et al, Unsupervised cross-domain image generation,
2016
Application of GANs
Domain adaptation
• Bousmalis et al, Unsupervised Pixel-Level Domain Adaptation
with Generative Adversarial Networks, 2016
Application of GANs
Domain adaptation
• Bousmalis et al : Results
Application of GANs
Domain adaptation
• Shrivastava et al, Learning from simulated and unsupervised
images through adversarial training, 2016
Application of GANs
Domain adaptation
• Shrivastava et al : Results
Application of GANs
Domain adaptation
• Taeksoo Kim et al, Learning to Discover Cross-Domain
Relations with Generative Adversarial Networks, 2017
Application of GANs
Domain adaptation
• Extended Taigman’s idea
using smart bijective mapping
Application of GANs
Unsupervised clustering
• Jamonglab : https://github.com/buriburisuri/timeseries_gan
– Using InfoGAN
Application of GANs
Real Fake
Unsupervised clustering
Application of GANs
Text generation
• Yu et al, SeqGAN : Sequence Generative Adversarial Nets with
Policy Gradient, 2016
Application of GANs
Text generation
• Maddison et al, The concrete distribution : A continous
relaxation of discrete random variables, 2016
• Kusner et al, GANs for sequences of discrete elements with the
Gumbel-softmax distribution, 2016
Application of GANs
Text generation
• Zhang et al, Generating text via Adversarial Training, 2016
Application of GANs
Imitation learning ( IRL or Optimal control )
• Ho et al, Model-free imitation learning with policy optimization, 2016
• Finn et al, A connection between generative adversarial networks,
Inverse Reinforcement Learning, and Energy-based models, 2016
Application of GANs
Future of GANs
Pre-trained unified features
• Unified features by unsupervised learning
• Pre-trained and shipped on mobile chip as default
Future of GANs
E
(pre-trained
encoder)
z
x C
(classifier)
1(Good)
0(Bad)
Personalization at scale
• Environment ( User ) simulator
Future of GANs
Personalization at scale
• Intelligent agent using RL
Future of GANs
Personalization at scale
• For example, cardiac simulator using GAN
Future of GANs
Conclusion
• Unsupervised learning is next frontier in AI
– Same algos, multiple domains
• Creativity and experience are no longer human temperament
Towards Artificial General Intelligence
Future of GANs
Thank you.
http://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091

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