Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...

Abstract: In hyperspectral imaging systems, stripe noise is a common interference phenomenon, severely affecting the quality and usability of data. Although existing denoising methods have achieved ...

This project presents a comprehensive implementation of a Variational Autoencoder system designed for unsupervised anomaly detection in high-dimensional datasets. The implementation emphasizes ...

we propose a Hierarchical ST variational autoencoder (HiSTaR) to extract multi-level latent features of spots. HiSTaR tends to perform well in identifying spatial domains across multiple datasets from ...

ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...