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SinGAN: Learning a Generative Model from a Single Natural Image: Difference between revisions
SinGAN: Learning a Generative Model from a Single Natural Image (view source)
Revision as of 14:18, 8 November 2019
, 8 November 2019→Training and Loss Function
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===Adversarial Loss=== | ===Adversarial Loss=== | ||
They use the [https://arxiv.org/abs/1704.00028 WGAN-GP loss].<br> | They use the [https://arxiv.org/abs/1704.00028 WGAN-GP loss].<br> | ||
This drops the log from the traditional cross-entropy loss.<br> | |||
<math>\min_{G_n}\max_{D_n}L_{adv}(G_n, D_n)+\alpha L_{rec}(G_n)</math><br> | |||
The final loss is the average over all the patches.<br> | The final loss is the average over all the patches.<br> | ||
<syntaxhighlight lang="python"> | |||
# When training the discriminator | |||
netD.zero_grad() | |||
output = netD(real).to(opt.device) | |||
#D_real_map = output.detach() | |||
errD_real = -output.mean()#-a | |||
errD_real.backward(retain_graph=True) | |||
#... Make noise and prev ... | |||
fake = netG(noise.detach(),prev) | |||
output = netD(fake.detach()) | |||
errD_fake = output.mean() | |||
errD_fake.backward(retain_graph=True) | |||
# When training the generator | |||
output = netD(fake) | |||
errG = -output.mean() | |||
errG.backward(retain_graph=True) | |||
</syntaxhighlight> | |||
===Reconstruction Loss=== | ===Reconstruction Loss=== | ||