generative-models — Page 4

Vision-aided GAN

In recent years, computer scientists have been working on improving the performance of Generative Adversarial Networks (GANs), which are machine learning models capable of generating new data based on a training dataset. One way to improve the performance of GANs is through vision-aided training, which involves using pretrained computer vision models in an ensemble of discriminators. This technique allows the GAN to generate more accurate and diverse outputs, which is particularly useful in appl

VQ-VAE

A VQ-VAE is a type of variational autoencoder that is able to obtain a discrete latent representation for data. It differs from traditional VAEs in two ways: the encoder network outputs codes that are discrete rather than continuous and the prior is learned instead of being static. What is a Variational Autoencoder? A VAE is a type of neural network that is able to generate new data that is similar to the data fed into it. It uses a latent space to represent the input data and can be used for

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