IEEE

Distribution Features Based on Normalizing Flow for Anomaly Detection

Anomaly detection has shown significant potential in identifying and localizing defects in industrial production images. Existing methods focus on detecting local structural anomalies, such as cracks ...
Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...
IEEE

Anomaly Detection in GeoLLM Using Variational Autoencoder

Abstract: Geospatial-based Large Language Models are an emerging technology based on the geospatial locations and the different applications based on the locations. From the survey, it is seen that ...
GitHub

Variational autoencoder with Kolmogorov-Arnold Network for spatial domain detection.

Experiments were executed on NVIDIA A40 of 46068MiB memory in linux with torch==2.1.0+cu121, torch_geometric==2.3.1, torch-sparse==0.6.18+pt21cu121, and torchvision==0.16.0+cu121. The stkan is an ...
Scientific Research Publishing

An, J. and Cho, S. (2015) Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability. Special Lecture on IE, 2, 1-18.

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 ...
Scientific Research Publishing

Kingma, D.P. and Welling, M. (2019) An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning, 12, 307-392.

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 ...