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[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
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2017/6/13 Deep Learning JP: http://deeplearning.jp/seminar-2/
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Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
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☓ ☓ KL(P ||Q) =
P(x)log P(x) Q(x) dx∫
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KL(P ||Q) = P(x)logP(x)dx
− P(x)logQ(x)dx∫∫
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Qθ (y |
x) = Aexp − 1 2 y − fθ (x)( )2⎛ ⎝⎜ ⎞ ⎠⎟ − P(x)logQθ (x)dx∫ = EP −logQθ x( )⎡⎣ ⎤⎦ = EP −log A + 1 2 y − fθ (x)( )2⎡ ⎣⎢ ⎤ ⎦⎥
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Wp (µ,ν)p = inf π∈Γ(µ,ν
) c(x,y)p dπ(x,y)∫
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δ x −
a( )−∞ ∞ ∫ dx = 1 δ x( )= 0 x ≠ 0( ) ˆP(x) = 1 m δXi i=1 m ∑
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lp p P,Q( )= |
FP (x)− FQ (x)|p dx −∞ ∞ ∫
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W1(P,Q) = |
FP −1 ( 0 1 ∫ µ)− FQ −1 (µ)| dµ l1 P,Q( )= | FP (x)− FQ (x)| dx −∞ ∞ ∫
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c(x1,y1)+ c(x2,y2 )
< c(x1,y2 )+ c(x2,y1)
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