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 ...

Specifically, we demonstrate how exploring a variational autoencoder (VAE) latent space, trained on purely normal (valid) data, can effectively fuzz-test representational robustness by anomaly ...

PerturbNet is a generative AI model that can predict shifts in cell state—changes in overall gene expression—in response to ...

Variational Autoencoders (VAE) on MNIST By stuyai, taught and made by Otzar Jaffe This project demonstrates the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras on the ...

In this paper, we propose an identity-aware variational autoencoder (ID-VAE) based face swapping framework, dubbed VAFSwap, which learns disentangled identity and attribute representations for ...

Opinion When Generative Adversarial Networks (GANs) first demonstrated their capability to reproduce stunningly realistic 3D faces, the advent triggered a gold rush for the unmined potential of GANs ...