The code for "Text-to-image synthesis with self-supervised bi-stage generative adversarial network"
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Updated
Jun 17, 2023 - Python
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.
The code for "Text-to-image synthesis with self-supervised bi-stage generative adversarial network"
[2022/23] A study of Generative Adversarial Networks, including experiments using anime faces dataset.
GAN trained to produce hand gesture images.
Implementation of Generative Adversarial Networks paper plus training on tiny problems.
Simple Implementation of Generative Adversarial Network, which is one type of deep learning model to generate image. This project is implemented based on Tensorflow.
My collection of GAN implemented using MXNet
Keras Implementation of Generative Adverserial Networks
Code for ``A Point Set Generation Network for 3D Object Reconstruction from a Single Image''
falppy-bird machine-learning program using svm and GAN
Notes and exercises from book Generative Adversarial Networks
PyTorch implementation of the siamese architecture for style transfer I developed for my master's thesis.
A PyTorch Implementation of Goodfellow et al.'s Paper on Generative Adversarial Networks
This project comes from a Kaggle Competiton named Generative-Dog-Images. Deep Convolutional GAN (DCGAN) and Conditional GAN (cGAN) are applied to generate dog images. Created a model to randomly generate dog images which are not existed in the original dataset.
A Deep generative model that can generate art - trained on subreddits of art.
Simple DCGAN and ProgressiveGAN Implementation and GAN Testing GUI
A collection of different generative adversarial networks (GAN).
Story Teller is a Streamlit application that generates a story based on an input image. It utilizes the Hugging Face Transformers library and the Salesforce BLIP Image Captioning model.
Experimental GenAI project focused on emulating human creativity through mutagenic training algorithms.
Released June 10, 2014