Taller de ML (Aprendizaje de Máquina) para crear imágenes artísticas (Generative Art) con redes Adversarias Generadoras y Condicionadas (GAN/CGAN) con los datos MINST de moda (Fashion MINST).
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Mar 3, 2024 - HTML
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.
Taller de ML (Aprendizaje de Máquina) para crear imágenes artísticas (Generative Art) con redes Adversarias Generadoras y Condicionadas (GAN/CGAN) con los datos MINST de moda (Fashion MINST).
An unofficial PyTorch implementation of VQGAN
AI AtoZ
Built a real-time website for image generation using gan-cls algorithm. The algorithm is trained on CUBS 200 birds dataset.
Repository for Slide Deck and Code Examples for talk at SDP Convening 2023
Pix2Pix Implementation for Facade Dataset
A framework to synthsize Brain data using AI models
Python code + notebooks to fully reproduce the results for the blog post "These Bored Apes Do Not Exist" on Medium. Blog post URL: https://medium.com/@nathancooperjones/these-bored-apes-do-not-exist-6bed2c73f02c
Cardiac Fats Segmentation Using a Conditional Generative Adversarial Network
This is Udacity - Deep Learning Project 4 - Generate Faces
Its a image generation library which learns to generate patterns based on training data
Generated faces from a pair of multi-layer neural networks generator and discriminator that compete against each other until one learns to generate realistic images of faces using CelebFaces Attributes (CelebA) dataset.
Experiments with Baudelaire and a text-to-image GAN.
A generative adversarial network (GAN) that generates images of faces.
This is code for Speakup AI website
Projects for Udacity Deep Learning Nanodegree Program: CNN, RNN, GANs
Generate images to handle imbalanced datasets using DCGAN
Released June 10, 2014