A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in ...

This repository contains a Jupyter notebook implementing a Vanilla Variational Autoencoder (VAE) for image generation. The VAE is a powerful generative model that learns to encode images into a latent ...

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 ...