Personal notes about Deep Learning generative methods
-
Updated
Mar 5, 2023 - TeX
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Personal notes about Deep Learning generative methods
Text of Bachelor's Thesis - Generating Faces with Generative Adversarial Networks.
Generative Adversarial Networks(GANs) | Semester 7 Artificial Intelligence & Data Science Seminar files
Context Aware Super Resolution
Lecture notes for Probabilistic Graphical Models for Image Analysis, ETH Zurich fall 2018
A web application for generating music that sounds like the Grateful Dead.
CapsNet based DCGAN, B.E Capstone project
My Masters Thesis about cracking passwords with Deep Learning
The LaTeX codes of the paper that was accpeted to GECCO 2020.
B.Sc. Final Project: Generating adversarial examples using GAN (Generative Adversarial Network) in Pytorch on the MNIST dataset.
Code for the paper "Transductive Adversarial Networks (TAN)" by Sean Rowan (2018).
Training a GAN using superconductivity data
Machine Learning UIUC SP 2018
Generating music using quantum machine learning models. (QuGAN and QLSTM)
Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
Released June 10, 2014