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.
The official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot Patch-Based Training
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
PSGAN running with ncnn⚡妆容迁移/仿妆⚡Imitation Makeup/Makeup Transfer⚡
A Repository with C++ implementations of Reinforcement Learning Algorithms (Pytorch)
Building generative models for synthesizing and analyzing volume-rendered images
C++ implementation of neural networks library with Keras-like API. Contains majority of commonly used layers, losses and optimizers. Supports sequential and multi-input-output (flow) models. Supports single CPU, Multi-CPU and GPU tensor operations (using cuDNN and cuBLAS).
Unofficial implementation of StyleGAN's generator
Caffe/C++ implementation of Boundary Equilibrium Generative Adversarial Networks paper for face generation
DCGAN Implementation using PyTorch in both C++ and Python
GFPGAN face reconstruction with ncnn on a bare Raspberry Pi
Nebula: Deep Neural Network Benchmarks in C++
A MNIST GAN implemented from scratch in C++
Implementation of Face Portrait v2 Model on Android plaform using NCNN framework
Code du GAN du groupe Couleuvre (C++)
NLP Modeling for Paraphrase Generation
Naive implementation of the generative adversarial network (GAN) training written in c++ using mxnet cpp API
Released June 10, 2014