Image-to-Image Translation in PyTorch

Design and Implement a Generative Adversarial Network (GAN) to generate new images

Synthetic data generation for tabular data

Seminar project Unsupervised Image-to-Image translation using GANs

Synthetic data generators for tabular and time-series data

we use activation maximization and GANs to discover patterns that contributes to the concept of naturalness in satellite imagery

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

This repository comprises of the projects and assignments that i have completed during my tenure at Great Lakes for the course program PGP-AIML. This repository also includes the lab work thatwas done during the classes and even those that were given as assessments.

Gans Specialization course by deeplearning.ai: solved assignments and labs

A (clean) PyTorch implementation of CycleGAN on Horse2zebra dataset

Repositorio Ramo Machine Learning for Business Inteligence - Udec 2020-1 (Deep Learning with tensorflow)

Implementation of papers in 100 lines of code.

Generating different facial expressions.It uses diferent types of Generative Adversarial Network to generate the required outputs.

This code implements a DCGAN to generate realistic images of handwritten digits from the MNIST dataset, training the generator and discriminator alternatively for a set number of epochs.

PyTorch implementations of Generative Adversarial Networks

Generating Realistic Human Faces with Generative Networks

The respository deals with the task of Image Super Resolution using two differents approaches namely SR-GANs and SR-ResNets.

An implementation of a vanilla GAN in PyTorch.

Welcome to my homepage.

JGAN model zoo supports 27 kinds of mainstream GAN models with high speed for jittor.