Image Super resolution

Paper - Link

• Super-Resolution Generative Adversarial Network
• adversarial loss and perceptual loss
• Improve the network structure by introducing the Residual-in-Residual Dense Block (RDDB)
• Perceptual loss by using the VGG features
• Basic architecture

• Replace the original basic block with the proposed Residual-in-Residual Dense Block (RRDB)

GAN Loss functions

• minimax loss: The loss function used in the paper that introduced GANs.
• Wasserstein loss: The default loss function for TF-GAN Estimators. First described in a 2017 paper.

Ref - link

Demo and Sample results

Tensorflow colab code - link

• Bringing Old Photos Back to Life
• Training a StyleGAN3 in Colab | GAN | Create an NFT
• StyleGAN3 from NVIDIA
• How to Train StyleGAN2-ADA in Colab using Instagram Images
• Training NVIDIA StyleGAN2 ADA under Colab Free and Colab Pro Tricks
• StyleGAN2
• Training StyleGAN using Transfer learning on a custom dataset in google colaboratory
• Barbershop: Try Different Hairstyles and Hair Colors from Pictures (GANs)

Keep Exploring!!!