Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Classic Augmentation Based Classifier Generative Adversarial Network (CACGAN) for COVID-19 Diagnosis

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

pytorch implementation of Domain-Adversarial Training of Neural Networks

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

基于NanoDet项目进行小裁剪，专门用来实现PyTorch 版本的代码，下载直接能使用，支持图片、视频文件、摄像头实时目标检测。

StyleGAN2-ADA - Official PyTorch implementation

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training

Official Code for DragGAN (SIGGRAPH 2023)

MMD-GAN: Towards Deeper Understanding of Moment Matching Network

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN