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Generative Adversarial Network
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.
Keras implementations of Generative Adversarial Networks.
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Collection of generative models in Tensorflow
Learning Chinese Character style with conditional GAN
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun
Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks
Deep Learning in Haskell
Research Framework for easy and efficient training of GANs based on Pytorch
Generative Adversarial Transformers
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699
[IEEE TIP] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
Generative Adversarial Networks (GANs) resources sorted by citations
Speech Enhancement Generative Adversarial Network in TensorFlow
Official Implementation for "Only a Matter of Style: Age Transformation Using a Style-Based Regression Model" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02754
[CVPR 2019]: Pluralistic Image Completion
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
A collection of awesome video generation studies.
Keras implementation of "DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks"
Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression
Implementation of Papers on Adversarial Examples