Generating images in different contexts using GANs and Variational Autoencoders
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Updated
Sep 19, 2022 - Jupyter Notebook
Generating images in different contexts using GANs and Variational Autoencoders
The following study presents a model for generating chest X-ray images of normal subjects (without lung disease) and pneumonia patients.
GANs 101 and its Applications: An exploration of DCGAN, WGAN, LSGAN and GAN transfer learning on the CelebA dataset
TensorFlow Generative Adversarial Networks (GANs)
Repo of my master thesis at Pompeu Fabra University: "Towards album artwork generation based on audio". We analyze VAEs and GANs to condition image generation with audio.
AVmod is a Audiovisual modulator developed with Deep Fake
Improved LSGAN using simple loss constraint
Search for potential customers
Implementation of DCGAN and LSGAN by tensorflow, Train the GAN in Google Cloud Colab.
The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met
This repository compares the performance of DCGAN, WGAN, WGAN-GP and LSGAN in terms of sample quality and several other factors
A short demonstration of GANs learning a probability distribution
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