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.
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.
The following are the generative deep learning models that I have implemented to date:
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Autoencoder: 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.
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Conditional GAN: An extension of GANs that allows generating data conditioned on certain information.
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CycleGAN: A model for translating images from one domain to another, without the need for paired data.
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DCGAN (Deep Convolutional Generative Adversarial Networks): A variant of GANs that uses convolutional layers in its networks.
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GAN (Generative Adversarial Networks): GANs are a type of generative model that uses two neural networks, a generator and a discriminator, which are trained simultaneously.
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GAN Controllable Generation: A model that allows controlling the characteristics of the generated data.
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Neural Style Transfer: A model that applies the style of one image to another.
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Pix2Pix: A model for translating images from one domain to another.
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VAE (Variational Autoencoder): A type of autoencoder that produces a distribution of the input data rather than a single representation.
Contributions to this repository are welcome. If you have any questions or suggestions, please do not hesitate to contact me.
