Apr 5, 2024 · This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on …

Jan 1, 2021 · We designed two anomaly detectors - an Adversarial Autoencoder (AAE) and a Deep Convolutional Generative Adversarial Networks (DCGAN). These models are build up …

The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to …

4 days ago · A novel autoencoder (SESAA) is proposed in this work that combines self-attention with squeeze-and-excitation (SE) blocks and improves video anomaly detection by using a …

Jan 29, 2020 · In this article, I explain how autoencoders combined with kernel density estimation can be used for image anomaly detection even when the training set is comprised only of …

This paper utilizes convolutional autoencoders (Conv-AE) for the sake of anomaly detection based on the DPMU measure-ments in distribution systems. The Conv-AE is unsupervised …

We applied convolutional versions of a “standard” au-toencoder (CAE), a variational autoencoder (VAE) and an adversarial autoencoder (AAE) to two different publicly available datasets and …

Sep 1, 2024 · Deep learning architectures, particularly Variational Auto-encoders (VAEs) and Generative Adversarial Networks (GANs), have emerged as powerful tools for producing …

In manufacturing industries, it is of fundamental importance to detect anomalies in production in order to meet the required quality goals and to limit the number of defective products that are …

We present a combination of advanced data mining and machine-learning (ML)-based techniques. It combines statistical and unsupervised convolutional autoencoders to provide a …