A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...

Stable Diffusion uses a variational autoencoder (VAE) to generate detailed images from a caption with only a few words. Unlike prior autoencoder-based diffusion models, Stable Diffusion incorporates a ...

Kaizen rethinks cell segmentation by mimicking brain predictions. Using an iterative machine-learning approach to refine boundaries in crowded microscopy images, it enhances accuracy in tissue studies ...