generative-adversarial-network

Official Implementation for "Pivotal Tuning for Latent-based editing of Real Images" (ACM TOG 2022) https://arxiv.org/abs/2106.05744

TextGAN is a PyTorch framework for Generative Adversarial Networks (GANs) based text generation models.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

InstaGAN: Instance-aware Image Translation (ICLR 2019)

PyTorch code for "PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer" (CVPR 2020 Oral)

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets