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)
Caffe/C++ implementation of Boundary Equilibrium Generative Adversarial Networks paper for face generation
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
Inference of a train CycleGAN model applied to a live viewfinder. Using TFLite
DCGAN Implementation using PyTorch in both C++ and Python
Nebula: Deep Neural Network Benchmarks in C++
NLP Modeling for Paraphrase Generation
Implementation of Face Portrait v2 Model on Android plaform using NCNN framework
GFPGAN face reconstruction with ncnn on a bare Raspberry Pi
Code du GAN du groupe Couleuvre (C++)
A MNIST GAN implemented from scratch in C++
Released June 10, 2014