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[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients

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Deep Learning JP
Deep Learning JP

2017/6/13 Deep Learning JP: http://deeplearning.jp/seminar-2/

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☓ ☓ KL(P ||Q) = P(x)log P(x) Q(x) dx∫
KL(P ||Q) = P(x)logP(x)dx − P(x)logQ(x)dx∫∫
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⎡ ⎣⎢ ⎤ ⎦⎥
Wp (µ,ν)p = inf π∈Γ(µ,ν ) c(x,y)p dπ(x,y)∫
δ x − a( )−∞ ∞ ∫ dx = 1 δ x( )= 0 x ≠ 0( ) ˆP(x) = 1 m δXi i=1 m ∑
lp p P,Q( )= | FP (x)− FQ (x)|p dx −∞ ∞ ∫
W1(P,Q) = | FP −1 ( 0 1 ∫ µ)− FQ −1 (µ)| dµ l1 P,Q( )= | FP (x)− FQ (x)| dx −∞ ∞ ∫
c(x1,y1)+ c(x2,y2 ) < c(x1,y2 )+ c(x2,y1)
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients

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