Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
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Updated
Mar 20, 2018 - Lua
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
An implementation of context encoders by Deepak Pathak with a remodeled discriminator.
Generate cat images with neural networks
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875
Semi-supervised InfoGAN
Reversing GAN image generation for similarity search and error/artifact fixing
Image completion with Torch
Add colors to black and white images with neural networks (GANs).
Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network
Handwritten Chinese Characters Generation
A torch implementation of "Pixel-Level Domain Transfer"
Image De-raining Using a Conditional Generative Adversarial Network
Generate cat images with neural networks
Released June 10, 2014