[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.

Using Machine Learning to predict the type of exercise from movement data

[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Spectral Normalization for Keras Dense and Convolution Layers

Privacy-preserving generative deep neural networks support clinical data sharing

Tensorflow re-implementation of GAN for text summarization

GAAL-based Outlier Detection

Projecting images to latent space with StyleGAN2.

🔥3D-RecGAN++ in Tensorflow (TPAMI 2018)

Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation

Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation"

An official PyTorch implementation of the paper "Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language", NeurIPS 2018

GANs for time series generation in pytorch

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019)

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

A web porting for NVlabs' StyleGAN.

A Pytorch implementation of Stylegan2 with UNet Discriminator