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 ...

In contrast, we introduce the Capsule Enhanced Variational AutoEncoder (CE-VAE), a novel architecture designed to efficiently compress and enhance degraded underwater images. Our attention-aware image ...

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 ...

Figure 1: Overview of the Constrained Subspace Variational Autoencoder (CS-VAE). The latent space is divided in three parts: (1) the supervised latents decode the labeled body positions, (2) the ...

In short, it improves the generated images. A VAE is trained for certain aspects of the image, and the default VAE bundled in our UI (vae-ft-mse-840000-ema-pruned) improves the eyes in generated ...

To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a ...

For both cases, the variational autoencoder with 4 hidden layers reached the lowest values. This indicates that 4-layer VAE is capable of generating protein conformations that are closer to the ...