https://github.com/jersongb22/generativedeeplearning
Links to my works, where a variety of generative models are implemented using TensorFlow and PyTorch. Among the implemented models are Autoencoder, VAE, GAN, Pix2Pix, among others.
https://github.com/jersongb22/generativedeeplearning
autoencoder cnn cyclegan gan generative-deep-learning neural-style-transfer pix2pix plotly python pytorch tensorflow u-net vae vgg-19
Last synced: 4 months ago
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Links to my works, where a variety of generative models are implemented using TensorFlow and PyTorch. Among the implemented models are Autoencoder, VAE, GAN, Pix2Pix, among others.
- Host: GitHub
- URL: https://github.com/jersongb22/generativedeeplearning
- Owner: JersonGB22
- Created: 2024-04-06T21:50:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-24T19:04:43.000Z (about 1 year ago)
- Last Synced: 2025-01-25T11:25:43.958Z (6 months ago)
- Topics: autoencoder, cnn, cyclegan, gan, generative-deep-learning, neural-style-transfer, pix2pix, plotly, python, pytorch, tensorflow, u-net, vae, vgg-19
- Homepage:
- Size: 940 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
#
**Generative Deep Learning Models**
This repository contains a collection of links to my repositories, which showcase implementations of generative deep learning models in Python, using the Tensorflow and Pytorch libraries.
## **What is Generative Deep Learning?**
Generative Deep Learning is a subfield of Artificial Intelligence that uses neural networks to generate new data that resemble the training data. These models can generate a variety of data, including images, sounds, text, and more.
## **Implemented Models**
The following are the generative deep learning models that I have implemented to date:
1. [**Autoencoder**](https://github.com/JersonGB22/Autoencoder-TensorFlow-PyTorch): An autoencoder is a neural network that is trained to copy its input to its output. It is used to learn efficient representations of the input data and/or to reduce the dimension of the input data.
2. [**Conditional GAN**](https://github.com/JersonGB22/ConditionalGAN-TensorFlow-PyTorch): An extension of GANs that allows generating data conditioned on certain information.
3. [**CycleGAN**](https://github.com/JersonGB22/CycleGAN-TensorFlow-PyTorch): A model for translating images from one domain to another, without the need for paired data.
4. [**DCGAN (Deep Convolutional Generative Adversarial Networks)**](https://github.com/JersonGB22/DCGAN-TensorFlow-PyTorch): A variant of GANs that uses convolutional layers in its networks.
5. [**GAN (Generative Adversarial Networks)**](https://github.com/JersonGB22/GAN-TensorFlow-PyTorch): GANs are a type of generative model that uses two neural networks, a generator and a discriminator, which are trained simultaneously.
6. [**GAN Controllable Generation**](https://github.com/JersonGB22/GANControllableGeneration-TensorFlow-PyTorch): A model that allows controlling the characteristics of the generated data.
7. [**Neural Style Transfer**](https://github.com/JersonGB22/NeuralStyleTransfer-TensorFlow-PyTorch): A model that applies the style of one image to another.
8. [**Pix2Pix**](https://github.com/JersonGB22/Pix2Pix-TensorFlow-PyTorch): A model for translating images from one domain to another.
9. [**VAE (Variational Autoencoder)**](https://github.com/JersonGB22/VAE-TensorFlow-PyTorch): A type of autoencoder that produces a distribution of the input data rather than a single representation.
## **Contributions**
Contributions to this repository are welcome. If you have any questions or suggestions, please do not hesitate to contact me.
## **Technological Stack**
[](https://docs.python.org/3/)
[](https://www.tensorflow.org/api_docs)
[](https://pytorch.org/docs/stable/index.html)
[](https://plotly.com/)
## **Contact**
[](mailto:[email protected])
[](https://www.linkedin.com/in/jerson-gimenes-beltran/)
[](https://github.com/JersonGB22/)