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Generative Adversarial Network

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.

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Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

  • Updated May 23, 2024
  • Jupyter Notebook

This repository contains notebooks showcasing various generative models, including DCGAN and VAE for anime face generation, an Autoencoder for converting photos to sketches, a captioning model using an attention mechanism for an image caption generator, and more.

  • Updated Oct 31, 2023
  • Jupyter Notebook

Released June 10, 2014

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