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.
real time face swap and one-click video deepfake with only a single image
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
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GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
Image-to-Image Translation in PyTorch
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A list of all named GANs!
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch:入门与实战》)
A MNIST-like fashion product database. Benchmark 👇
Image-to-image translation with conditional adversarial nets
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Keras implementations of Generative Adversarial Networks.
so-vits-svc fork with realtime support, improved interface and more features.
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PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
Released June 10, 2014