Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
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Updated
Nov 19, 2018 - Jupyter Notebook
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
Improving MMD-GAN training with repulsive loss function
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥
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.
Unofficial PyTorch implementation of "Progressive Growing of GANs for Improved Quality, Stability, and Variation".
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.
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.
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