Keras implementations of Generative Adversarial Networks.
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Updated
Dec 12, 2022 - Python
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.
Collection of generative models in Tensorflow
Learning Chinese Character style with conditional GAN
Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks
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
Speech Enhancement Generative Adversarial Network in TensorFlow
Keras implementation of "DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks"
[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
Research Framework for easy and efficient training of GANs based on Pytorch
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
Generative Adversarial Transformers
[CVPR 2019]: Pluralistic Image Completion
Official Implementation for "Only a Matter of Style: Age Transformation Using a Style-Based Regression Model" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02754
SteganoGAN is a tool for creating steganographic images using adversarial training.
Implementation of Papers on Adversarial Examples
Code repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'
Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression
Code and Data Release for GenRe (NeurIPS 2018) and ShapeHD (ECCV 2018)
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
Released June 10, 2014