Super-resolution GANs for improving the texture resolution of old games.
It is what it is. #GAN to enhance textures in old games making them look better.
ArXiV: https://arxiv.org/abs/1809.00219
Link: https://www.gamespot.com/forums/pc-mac-linux-society-1000004/esrgan-is-pretty-damn-amazing-trying-max-payne-wit-33449670/
#gaming #superresolution
🔗 ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic an
It is what it is. #GAN to enhance textures in old games making them look better.
ArXiV: https://arxiv.org/abs/1809.00219
Link: https://www.gamespot.com/forums/pc-mac-linux-society-1000004/esrgan-is-pretty-damn-amazing-trying-max-payne-wit-33449670/
#gaming #superresolution
🔗 ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic an
arXiv.org
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated...
Play with #GAN(Generative Adversarial Networks) in your browser and better understand what's going on inside network
https://poloclub.github.io/ganlab/?fbclid=IwAR1xl1kmA4DflkXShjbufAD4EUOW6O9TxFcoBPI-DWHClIR4UhSD566d4XY
🔗 GAN Lab: Play with Generative Adversarial Networks in Your Browser!
https://poloclub.github.io/ganlab/?fbclid=IwAR1xl1kmA4DflkXShjbufAD4EUOW6O9TxFcoBPI-DWHClIR4UhSD566d4XY
🔗 GAN Lab: Play with Generative Adversarial Networks in Your Browser!
📹Artificial caricature
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
#GAN #CelebA #DL
🔗 Unsupervised Doodling and Painting with Improved SPIRAL
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
#GAN #CelebA #DL
🔗 Unsupervised Doodling and Painting with Improved SPIRAL
arXiv.org
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with...
Data Science / Machine Learning / AI / Big Data (VK)
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis
Sergei Belousov: https://arxiv.org/abs/2104.04767
#DeepLearning #GenerativeAdversarialNetworks #GAN
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis
Sergei Belousov: https://arxiv.org/abs/2104.04767
#DeepLearning #GenerativeAdversarialNetworks #GAN