ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018) implemented in Tensorflow 2.0+. This is an unofficial implementation. With Colab.
A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network"
Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch
Image Super-Resolution Using Deep Convolutional Networks in Tensorflow https://arxiv.org/abs/1501.00092v3
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
Upscale an image by a factor of 4, while generating photo-realistic details.
pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
A PyTorch implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
