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Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Light NodeJS rate limiting and response delaying using Redis - including Express middleware.
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥
Improving MMD-GAN training with repulsive loss function
This repository contains my full work and notes on Deeplearning.ai GAN Specialization (Generative Adversarial Networks)
Demonstrates the discriminator field (shared schema) based multi-tenant application using Spring Boot & Hibernate 5.
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
Improved training of Wasserstein GANs
Simple way to serialize and deserialize polymorphic types for Json.NET
Adversarial Semantic Scene Completion from a Single Depth Image, accepted in 3DV 2018
This repo contains the code that generates Digimon images using the concept of GAN
A DCGAN built on the CIFAR10 dataset using pytorch
In this repository, I have developed a CycleGAN architecture with embedded Self-Attention Layers, that could solve three different complex tasks. Here the same principle Neural Network architecture has been used to solve the three different task. Although truth be told, my model has not exceeded any state of the art performances for the given task, but the architecture was powerful enough to understand the task that has been given to solve and produce considerably good results.
We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.
This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman
Demonstration of using polymorphic JSON types with golang.
AI that generates human faces which have never been seen before. The future is now 😁
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
AI-enabled Interior decoration, powered by CycleGANs. Built for @NextTechLabAP's #9Hacks!
Generating super-resolution images using GANs
Changes your discord username to get a different discriminator
Implementation of Wasserstein Generative Adversarial Networks using Tensorflow
A DCGAN built on the MNIST dataset using pytorch
Generate fake faces using generative adversarial network
Code for Curriculum based Dropout Discriminator for Domain Adaptation(CD3A), BMVC, 2019
An unofficial pytorch implementation for OGAN
A Generative Adversarial Network (GAN) trained on the MNIST dataset, capable of creating fake but realistic looking MNIST digit images that appear to be drawn from the original dataset.
Creating artificial images with a Generative Adversarial Network written in PyTorch.
This is a new alogorithm named WGAN, an alternative to traditional GAN training!