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 ...

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ...

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 ...

We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic ...

Variational Autoencoder in tensorflow and pytorch Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more ...

The purely perceptual computations of “affect-less” visual machines are sufficient for explaining the majority of explainable variance in human affective responses to images.

We call our approach Variational Autoencoder Modular Bayesian Network (VAMBN). Due to its generative nature, VAMBN allows for simulating virtual subjects by first drawing a sample from the BN and ...