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car

Content adaptive resampler for image downscaling

edsr-pytorch

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

first-order-model

This repository contains the source code for the paper First Order Motion Model for Image Animation

gitrob

Reconnaissance tool for GitHub organizations

han

PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

image-super-resolution

🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

lenet-on-mnist

My implementaion of LeNet Architecture which is trained on MNIST dataset for handwritten digit recognition.

mnist_gan

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

osengine

Open Source algo trading platform

pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

pytorch-gans

My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN, cGAN, DCGAN, etc.

qq

A Deep Learning based project for colorizing and restoring old images (and video!)

sql-metadata

Uses tokenized query returned by python-sqlparse and generates query metadata

srgan

My from-scratch implementation of an SRGAN in Pytorch that generates super resolution images upscaled by a factor of 4.

srgan-pytorch

A PyTorch implementation of SRGAN specific for Anime Super Resolution based on "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network". And another PyTorch WGAN-gp implementation of SRGAN referring to "Improved Training of Wasserstein GANs".

stock_price_predictor

Stock Price Predictor with Deep Learning

stylegan2

StyleGAN2 - Official TensorFlow Implementation

super-mario-reinforcement-learning

Double Q-learning reinforcement learning agent on NES Super Mario Bros

tecogan

This repo will contain source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN

urlscanio

CLI tool which uses URLScan to scan websites and download corresponding screenshots and DOMs.

vgg-16

Implementing VGG16 from scratch in Pytorch

virustotal-postman-collection

This repo contains a Postman collection for interacting with the VirusTotal Public API.