Anomaly detection in images is rapidly emerging as a critical field in both industrial quality control and medical diagnostics. Leveraging deep learning techniques, researchers have developed methods ...

Anomaly detection is the process of identifying events or patterns that differ from expected behavior. Anomaly detection can range from simple outlier detection to complex machine learning algorithms ...

The proposed industrial anomaly detection model is computationally efficient, memory-friendly, and also suitable for low-light conditions, common in manufacturing environments, making it well-suited ...

Unsupervised learning is a powerful type of machine learning where algorithms analyse and find patterns in data without any human intervention or prior knowledge of categories. Unlike supervised ...

Analyzing real life cases, it’s easy to notice that the issue of detecting anomalies is usually met in the context of various fields of application, including but not limited to intrusion detection, ...

Nathan Eddy works as an independent filmmaker and journalist based in Berlin, specializing in architecture, business technology and healthcare IT. He is a graduate of Northwestern University’s Medill ...