Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Variational. Variational autoencoders operate by making assumptions about how the latent variables of the data are distributed. A variational autoencoder produces a probability distribution for the ...

Training a Variational Autoencoder Training a VAE involves two measures of similarity (or equivalently measures of loss). First, you must measure how closely the reconstructed output matches the ...

More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out ...

A variational autoencoder (Kingma and Welling, 2013; Doersch, 2016) consists of an encoder and a decoder. We propose the following architecture for them. The encoder consists of a convolutional and a ...

Therefore, we propose Purifying Variational AutoEncoder (PuVAE), a method to purify adversarial examples. The proposed method eliminates an adversarial perturbation by projecting an adversarial ...

Variational Autoencoder (VAE) and Variational Bayesian methods Going through the code is almost the best way to explain the Variational Autoencoder. However, to fully understand Variational Bayesian ...