SinGAN: Learning a Generative Model from a Single Natural Image: Difference between revisions
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==Training and Loss Function== | ==Training and Loss Function== | ||
<math>\min_{G_n} \max_{D_n} \mathcal{L}_{adv}(G_n, D_n) + \alpha \mathcal{L}_{rec}(G_n)</math><br> | |||
They use a combination of the standard GAN adversarial loss and a reconstruction loss. | They use a combination of the standard GAN adversarial loss and a reconstruction loss. | ||
===Reconstruction Loss=== | ===Reconstruction Loss=== | ||
<math> | <math>\mathcal{L}_{rec} = \Vert G_n(0,(\bar{x}^{rec}_{n+1}\uparrow^r) - x_n \Vert ^2</math><br> | ||
The reconstruction loss ensures that the original image can be built by the GAN.<br> | The reconstruction loss ensures that the original image can be built by the GAN.<br> | ||
Rather than inputting noise to the generators, they input | Rather than inputting noise to the generators, they input | ||
<math>\{z_N^{rec}, z_{N-1}^{rec}, ..., z_0^{rec}\} = \{z^*, 0, ..., 0\}</math> | <math>\{z_N^{rec}, z_{N-1}^{rec}, ..., z_0^{rec}\} = \{z^*, 0, ..., 0\}</math> | ||
where the initial noise <math>z^*</math> is drawn once and then fixed during the rest of the training. | where the initial noise <math>z^*</math> is drawn once and then fixed during the rest of the training. | ||
Revision as of 19:33, 5 November 2019
SinGAN
Paper
Website
Github Official PyTorch Implementation
SinGAN: Learning a Generative Model from a Single Natural Image
Basic Idea
Bootstrap patches of the original image and build GANs which can add fine details to blurry patches at different path sizes.
- Start by building a GAN to generate low-resolution versions of the original image
- Then upscale the image and build a GAN to add details to patches of your upscaled image
- Fix the parameters of the previous GAN. Upscale the outputs and repeat.
Architecture
They build \(\displaystyle N\) GANs.
Each GAN \(\displaystyle G_n\) adds details to patches of the image produced by GAN \(\displaystyle G_{n+1}\) below it.
The final GAN \(\displaystyle G_0\) adds only fine details.
Generator
Discriminator
Training and Loss Function
\(\displaystyle \min_{G_n} \max_{D_n} \mathcal{L}_{adv}(G_n, D_n) + \alpha \mathcal{L}_{rec}(G_n)\)
They use a combination of the standard GAN adversarial loss and a reconstruction loss.
Reconstruction Loss
\(\displaystyle \mathcal{L}_{rec} = \Vert G_n(0,(\bar{x}^{rec}_{n+1}\uparrow^r) - x_n \Vert ^2\)
The reconstruction loss ensures that the original image can be built by the GAN.
Rather than inputting noise to the generators, they input
\(\displaystyle \{z_N^{rec}, z_{N-1}^{rec}, ..., z_0^{rec}\} = \{z^*, 0, ..., 0\}\)
where the initial noise \(\displaystyle z^*\) is drawn once and then fixed during the rest of the training.