論文紹介 Semi-supervised Learning with Deep Generative Models
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Transcript of 論文紹介 Semi-supervised Learning with Deep Generative Models
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Semi-supervised Learning
with Deep Generative ModelsNIPS2014 @ , 2015/01/20
Preferred Networks,
@beam2d
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l
l
(semi-supervised learning)
2
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4
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Transductive SVM
Manifold Tangent Classifier (MTC), AtlasRBF
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MNIST 100 3.33%
SVHN NORB
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x
z
p(x, z) = p(z)p(x|z)
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M1
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Neural Net
z N (z;0, I)
(,)
x N (x|, diag2)
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M1
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Neural Net
z N (z;0, I)
x Bernoulli(x|)
Gaussian Bernoulli
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M2Gaussian
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Neural Net
z N (z;0, I)
(,)
y Cat(y|)
x N (x|, diag2)
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AutoEncoder
l
l NN
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p(z)p(x|z) x z
p(z)p(x|z) q(x)q(z|x)
z
x
NN( ) NN( )
q(x)
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NN
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l M1
l M2
q(z|x) = N (z|(x), diag2(x)).NN
NN
q(z|y,x) = N (z|(y,x), diag2(y,x)),q(y|x) = Cat(y|(x)).
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M1 AutoEncoder
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log p(x) Eq(z|x)[log p(x|z)]KL[q(z|x)p(z)]
q(x, z) = p(x, z)
TSVM M2
z q(z|x)
AutoEncoder z
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M2
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log p(x, y) L(x, y) :=Eq(z|x,y)[log p(x|y, z) + log p(y) + log p(z) log q(z|x, y)]
log p(x) U(x) :=Eq(y,z|x)[log p(x|y, z) + log p(y) + log p(z) log q(y, z|x)]
(x,y):labeled
L(x, y) +
x:unlabaled
U(x)
(x,y):labeled
log q(y|x)
q(y|x)
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SGVB (SBP)
l
l
l Gaussian
Stochastic Gradient Variational Bayes Stochastic BackProp ICLR14, ICML14
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Eq(z|x,y)Eq(z|x,y)[f(x, y, z)]
Eq(z|x,y)[f(x, y, z)] = EN (|0,I)[f(x, y,(x) + (x) )]
Eq(z|x,y)[f(x, y, z)] = EN (|0,I)[f(x, y,(x) + (x) )]
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SGVB(SBP) +
l OK
l AdaGrad RMSprop
3.2 4.4 4.4
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2 2
l (MNIST, SVHN, NORB)
l 2
2 (MNIST)
(MNIST, SVHN)
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z zyx|y, zy x|y, zx
z|x
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%
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2 (style)
zz
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10
zx|y, z
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l
l
l
l DBM NN
l DBM
l
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Kingma, D. P., Mohamed, S., Jimenez Rezende, D., & Welling, M. (2014). Semi-supervised Learning with Deep Generative Models. In Advances in Neural Information Processing Systems 27 (pp. 35813589).
Stochastic Gradient VB AutoEncoder
Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes.International Conference on Learning Representations.
Stochastic BackProp
Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of the 31st International Conference on Machine Learning (pp. 12781286).
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