Paper :https://arxiv.org/pdf/1701.07875.pdf Code :https://github.com/igul222/improved_wgan_training 参考：https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html https://vincentherrmann.github.io/blog/wasserstein/
得到目标概率密度一般就利用极大似然估计的方法，而不同分布之间则一般用散度衡量。
模型生成得到的分布与原始真实的分布不太可能有交叉的地方。两个分布都仅仅只是各自有各自的，而不是联合的，得到的这种形式的目标分布是不理想的。It is then unlikely that…have a non-negligible intersection.
所以很多文献都是通过给目标分布添加噪声来尽量覆盖所有的例子，但是会使图像受损。
而GAN就是通过生成器让低维流形产生高维的分布，当下效果也不是很理想。
主要目标是衡量分布之间的距离 。we direct our attention on the various ways to measure how close the model distribution and the real distribution are, or equivalently.
研究了EM距离。we provide a comprehensive theoretical analysis of how the Earth Mover (EM) distance behaves in comparison to popular probability distances and divergences used in the context of learning distributions.
定义了WGAN。we de fine a form of GAN called Wasserstein-GAN that minimizes a reasonable and efficient approximation of the EM distance, and we theoretically show that the corresponding optimization problem is sound.
各种d i s t a n c e s ( d i v e r g e n c e s ) distances(divergences) T V \mathbf{TV} K L \mathbf{KL} J S \mathbf{JS} f f E a r t h Earth M o v e r ( E M ) Mover(EM) W ( P r , P g ) = inf γ ∈ Π ( P r , P g ) E ( x , y ) ∼ γ [ ∥ x − y ∥ ] = inf γ ∈ Π ( P r , P g ) ∫ ∫ [ γ ( x , y ) ∥ x − y ∥ ] d x d y (1)
\begin{aligned}
W(\mathbb{P}_{r},\mathbb{P}_{g})&=\inf_{\gamma \in \Pi(\mathbb{P}_{r},\mathbb{P}_{g})} \mathbb{E}_{(x,y) \sim \gamma} \left[\|x-y \| \right]\\
&=\inf_{\gamma \in \Pi(\mathbb{P}_{r},\mathbb{P}_{g})} \int \int \left[\ \gamma(x,y)\|x-y \| \right]\mathrm{d}x\mathrm{d}y
\tag{1}
\end{aligned}
P r , P g \mathbb{P}_{r},\mathbb{P}_{g} Π \Pi γ \gamma γ \gamma ( x , y ) (x,y) Π \Pi γ \gamma E a r t h Earth M o v e r ( E M ) Mover(EM)
假设有均匀分布Z ∼ U [ 0 , 1 ] Z \sim U[0,1] P 0 ∼ ( 0 , Z ) ∈ R 2 P_0 \sim (0,Z)\in \mathbb{R}^2 y y 0 0 1 1 g θ ∼ ( θ , Z ) g_\theta \sim(\theta,Z) P 0 P_0 ∀ ( x , y ) ∈ P , x = 0 and y ∼ U ( 0 , 1 ) ∀ ( x , y ) ∈ Q , x = θ , 0 ≤ θ ≤ 1 and θ , y ∼ U ( 0 , 1 ) (2)
\forall (x, y) \in P, x = 0 \text{ and } y \sim U(0, 1) \tag{2} \\
\forall (x, y) \in Q, x = \theta, 0 \leq \theta \leq 1 \text{ and } \theta, y \sim U(0, 1) \\
θ = 0 \theta=0 W W W ( P 0 , P θ ) = ∣ θ ∣ J S ( P 0 , P θ ) = { log 2 if θ ≠ 0 0 if θ = 0 K L ( P 0 , P θ ) = K L ( P θ , P 0 ) = { ∞ if θ ≠ 0 0 if θ = 0 T V ( P 0 , P θ ) = { 1 if θ ≠ 0 0 if θ = 0 where: D K L ( P ∥ Q ) = ∑ x = 0 , y ∼ U ( 0 , 1 ) 1 ⋅ log 1 0 = + ∞ D J S ( P ∥ Q ) = 1 2 ( ∑ x = 0 , y ∼ U ( 0 , 1 ) 1 ⋅ log 1 1 / 2 + ∑ x = 0 , y ∼ U ( 0 , 1 ) 1 ⋅ log 1 1 / 2 ) = log 2 (3)
\begin{aligned}
W(\mathbb{P}_{0},\mathbb{P}_{\theta})&=|\theta|\\
\mathbf{JS}(\mathbb{P}_{0},\mathbb{P}_{\theta})&= \begin{cases}
\log 2& \text{if $\theta \neq$0}\\
0& \text{if $\theta=$0}
\end{cases} \\
\mathbf{KL}(\mathbb{P}_{0},\mathbb{P}_{\theta})&=\mathbf{KL}(\mathbb{P}_{\theta},\mathbb{P}_{0})= \begin{cases}
\infty& \text{if $\theta \neq$0}\\
0& \text{if $\theta=$0}
\end{cases} \\
\mathbf{TV}(\mathbb{P}_{0},\mathbb{P}_{\theta})&= \begin{cases}
1 & \text{if $\theta \neq$0}\\
0& \text{if $\theta=$0}
\end{cases} \\
\text{where: $D_{KL}(P \| Q$) }& \text{$= \sum_{x=0, y \sim U(0, 1)} 1 \cdot \log\frac{1}{0} = +\infty$ } \\
\text{ $D_{JS}(P \| Q$)}&= \text{$\frac{1}{2}(\sum_{x=0, y \sim U(0, 1)} 1 \cdot \log\frac{1}{1/2} + \sum_{x=0, y \sim U(0, 1)} 1 \cdot \log\frac{1}{1/2}) = \log 2$ } \\
\tag{3}
\end{aligned}
Why Wasserstein is indeed weak? （有待研究更新）J S \mathbf{JS} X \mathcal{X} R 2 \mathbb{R}^2 X ∈ R 2 \mathcal{X}\in \mathbb{R}^2 C b ( X ) C_b(\mathcal{X}) X \mathcal{X} R \mathbb{R} 函数 的空间（C b ( X ) C_b(\mathcal{X}) C b ( X ) = { f : X → R , f is continuous and bounded } (4)
\begin{aligned}
C_b(\mathcal{X}) &= \{ f:\mathcal{X} \rightarrow \mathbb{R}, &\text{$f$ is continuous and bounded} \}\\
\tag{4}
\end{aligned}
f ∈ C b ( X ) f \in C_b(\mathcal{X}) f f R 2 \mathbb{R}^2 assume: f m × n ⋅ X n × 1 = R m × 1 ∴ f m × n ⋅ X n × d = R m × d ∴ ∥ f ∥ ∞ = max x ∈ X ∣ f ( x ) ∣ (5)
\begin{aligned}
\text{assume:}f_{m \times n} \cdot \mathcal{X}_{n \times 1}= \mathbb{R}_{m \times 1} \\
\therefore f_{m \times n} \cdot \mathcal{X}_{n \times d}= \mathbb{R}_{m \times d} \\
\therefore \|f\|_{\infin} = \max_{x \in \mathcal{X}}|f(x)|
\tag{5}
\end{aligned}
( C b ( X ) (C_b(\mathcal{X}) ( C b ( X ) , ∥ ⋅ ∥ ) (C_b(\mathcal{X}),\| \cdot \| ) f ∞ f_\infin E × E ⟶ R ( x , y ) ↦ ∥ x − y ∥ ( x , y ) ↦ ∥ x − y ∥ (6)
\begin{aligned}
{\mathbb {E}}\times {\mathbb {E}}\longrightarrow {\mathbb {R}}
{\displaystyle \ (x,y)\mapsto \Vert x-y\Vert } \ (x,y)\mapsto \Vert x-y\Vert
\tag{6}
\end{aligned}
利用Kantorovich-Rubinstein对偶性，将推土距离转换如下（but why?有待研究更新），其中K K K-Lipschitz : ∣ f ( x 1 ) − f ( x 2 ) ∣ ≤ K ∣ x 1 − x 2 ∣ \text{K-Lipschitz}:\lvert f(x_1) - f(x_2) \rvert \leq K \lvert x_1 - x_2 \rvert W ( P r , P θ ) = 1 K sup ∥ f ∥ L ≤ K E x ∼ P r [ f ( x ) ] − E x ∼ P θ [ f ( x ) ] (7)
\begin{aligned}
W(\mathbb{P}_{r},\mathbb{P}_{\theta})= \frac{1}{K} \sup_{\| f \|_L \leq K} \mathbb{E}_{x \sim \mathbb{P}_{r}}[f(x)] - \mathbb{E}_{x \sim \mathbb{P}_{\theta}}[f(x)]
\tag{7}
\end{aligned}
K-Lipschitz \text{K-Lipschitz} { f w } w ∈ W \{ f_w \}_{w \in W} f f L ( P r , P θ ) = W ( P r , P θ ) = max w ∈ W E x ∼ p r [ f w ( x ) ] − E z ∼ p r ( z ) [ f w ( g θ ( z ) ) ] (8)
\begin{aligned}
L(\mathbb{P}_{r},\mathbb{P}_{\theta})=W(\mathbb{P}_{r},\mathbb{P}_{\theta})= \max_{w \in W} \mathbb{E}_{x \sim p_r}[f_w(x)] - \mathbb{E}_{z \sim p_r(z)}[f_w(g_\theta(z))]
\tag{8}
\end{aligned}
正如算法流程所述， 以便使用梯度下降，所以文中使用约束权重范围的方法，以防止改变权重造成很大的改变，确保1-Lipschitz \text{1-Lipschitz}
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标签： mathbb ,Pr ,text ,GAN ,theta ,Wasserstein ,aligned ,sim ,WGAN
来源： https://blog.csdn.net/qq_42192910/article/details/104524492
强大源
发布了29 篇原创文章 · 获赞 15 · 访问量 1万+
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