To speed up the optimization process, we transform the corresponding problem into a lower-dimensional latent space learned by a variational autoencoder. This is trained on a total of 6839 different 2D ...

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 ...

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 ...

The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation.

Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data which consist of an encoder and decoder.

Description of the block copolymer SAXS–SEM morphology characterization dataset, image data preprocessing procedures, python packages utilized and the usages of each package, the variational ...

To this end, we propose a multi-domain variational autoencoder framework consisting of multiple domain-specific branches and a latent space shared across all branches for cross-domain information ...