Abstract: Anomaly detection ... based architecture always struggles with over-generalization and over-fitting problems, leading to poor reconstruction performance on real defective samples. In this ...

Anomaly detection is a critical technique for ensuring the safe operation ... features and key structural features from the fused data by parallel computing using MLP and CNN, respectively. Finally, ...

This repository provides a PyTorch implementation of autoencoders (both Convolutional and MLP-based) for anomaly detection on time series waveform data (e.g., from CSV files).

Acceldata, a leading provider of data observability and agentic data management solutions, is announcing a new capability designed to amplify the power of agentic reasoning within the company's xLake ...

In industries, the detection of anomalies such as scratches, dents, and discolorations is crucial to ensure product quality ...

Agentic Data Management Use Cases Enabled by Adaptive AI Anomaly Detection: ...

(Nanowerk News) In a significant leap toward the future of diabetes care, INL researchers have developed a graphene-based biosensor capable of detecting glucose at attomolar levels—representing the ...

Lockheed Martin saw a stock price increase of 10% last week, potentially buoyed by its collaboration with Arquimea to enhance anomaly detection for Intelligence, Surveillance, and Reconnaissance ...