Jang et al. [36] introduced a Convolutional Variational Autoencoder (CVAE) that models the variability in ECG patterns through learned latent distributions, facilitating clustering and anomaly ...

ABSTRACT: Anomaly detection in complex crowd scenes is a challenging task due to the inherent variability in crowd behaviors, interactions, and scales. This paper proposes a novel hybrid model that ...

MST-VAE is an unsupervised learning approach for anomaly detection in multivariate time series. Inspired by InterFusion paper, we propose a simple yet effective multi-scale convolution kernels applied ...

Grapevine is one of the economically most important quality crops. The monitoring of the plant performance during the growth period is, therefore, important to ensure a high quality end-product. This ...

Abstract: Accurate detection of anomalies in multivariate time series ... To meet this challenge, we propose a Multi Scale Convolutional Variational Autoencoder (MSCVAE) to detect anomalies in ...