CycleGAN
目录
Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks paper
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方法
损失
对抗损失 Adversarial loss
\(\mathcal{L}_{GAN}(G,D_Y,X,Y)=\mathbb{E}_{y \backsim p_{data}(y)}[\log D_Y(y)] +\mathbb{E}_{x \backsim p_{data}(x)}[\log(1-D_Y(G(x)))]\)
循环一致性损失 Cycle Consistency Loss
\(\mathcal{L}_{cyc}(G,F)= \mathbb{E}_{x \backsim p_{data}(x)}[\|F(G(x))-x\|_1] +\mathbb{E}_{y \backsim p_{data}(y)}[\|G(F(y))-y\|_1]\)
总损失
\(\mathcal{L}(G,F,D_X,D_Y)=\mathcal{L}_{GAN}(G,D_Y,X,Y) + \mathcal{L}_{GAN}(F,D_X,X,Y)+\lambda\mathcal{L}_{cyc}(G,F)\)
判别器
70 × 70 PatchGANs
评估
AMT
Amazon Mechanical Turk
FCN
The FCN metric evaluates how interpretable the generated
photos are according to an off-the-shelf semantic segmentation algorithm (the fully-convolutional network, FCN)
Semantic segmentation metrics
Cityscapes benchmark
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