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.

NN based lossless compression

Unsupervised Domain Adaptation for Computer Vision Tasks

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Improving MMD-GAN training with repulsive loss function

Demonstrates the discriminator field (shared schema) based multi-tenant application using Spring Boot & Hibernate 5.

This repository contains my full work and notes on Deeplearning.ai GAN Specialization (Generative Adversarial Networks)

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

[CVPR 2023] GLeaD: Improving GANs with A Generator-Leading Task

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 ta…

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.

Transfer Learning on GANs

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.