UofTHacksVIII - Finance Productivity WebApplication + Generative Deep Learning (WGAN-GP) Tree Generator - Best use of Google Cloud Award
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Updated
Mar 3, 2021 - JavaScript
UofTHacksVIII - Finance Productivity WebApplication + Generative Deep Learning (WGAN-GP) Tree Generator - Best use of Google Cloud Award
Improving Deep Learning accuracy using Generative Adversarial Networks (GANs). Implemented in PyTorch
An implementation of one personal project to gain experience with Generative Adversarial Network models and in particular on Wasserstrein GAN with gradient penalty. The final application's purpose is to generate synthetic images given a food category.
Final group project of "Neural Networks and Deep Learning" course at University of Padova
Image inpainting
WGAN with GP on EEG using Keras
Implementation of DCGAN and WGAN with gradient penalty for generating morula images from random noises in pytorch
Neural Network and Deep Learning course
This project is an exploration of Generative Models (GM) and its capabilities, focusing on the generation of bicycle images using Wasserstein Generative Adversarial Networks (WGAN-GP) in conjunction with estimators and generators.
Repository process and positioning from init dataset
A walkthrough of two GAN implementations (DCGAN and WGAN_GP)
Improving the GAN Model into a More Effective WGAN-GP Model
WGAN-GP를 이용해 생성한 가짜 사람 피부로 학습을 시킨 아토피 중증도를 분류해주는 XceptionV2모델을 서빙하는 웹사이트입니다.
An attempt to generate human and anime faces using a WGAN
Implemention of wasserstein generative adversarial networks (wgan) with gradient penalty for super resolution task in pytorch
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