Generative Adversarial Network Implementations in Keras
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Updated
Feb 23, 2020 - Jupyter Notebook
Generative Adversarial Network Implementations in Keras
This repository provides tools to train and evaluate the Genome-AC-GAN model for generating realistic artificial human genomes.
This software can be used to convert a sequence of digits to a video where the hand-written representations of those digits are twining from one to the other.
An Auxiliary Classifier GAN (ACGAN) in pytorch to generate MNIST digits.
Personal repository to learn about different types of GAN models.
Research for text-to-image synthesis via modified auxiliary classifier GANs. Incremental modification of model architecture for improved results, fully documented.
Classic Augmentation Based Classifier Generative Adversarial Network (CACGAN) for COVID-19 Diagnosis
A Simple code to train a CNN to predict label of Covid and Non-Covid CT scan images and an ACGAN to generate them.
This is the repository of Deep Learning for Computer Vision at National Taiwan University.
Memory Replay GANs: learning to generate images from new categories without forgetting
👩🦰 An ACGAN to generate anime faces with specific hair and eyes color
Playing with MNIST. Machine Learning. Generative Models.
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10
Generate anime face using Auxiliary classifier Generative Adversarial Networks
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