GitHub

[WACV2025] AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

This is the official code to reproduce the experiments in the paper AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2, accepted at IEEE/CVF Winter Conference on Applications of ...
IEEE

DA-VAE: A Semi-Supervised Approach for Anomaly Detection in Microstructure Imaging

Abstract: The DA-VAE model is a semi-supervised anomaly detection framework designed to identify defects in microstructure images, especially in contexts where defective samples are scarce. In ...
Scientific Research Publishing

Prototype-Based Dynamic Autoencoders for Real-Time Anomaly Detection and Explainability ()

Real-time detection of anomalies in data streams is a foundation of modern applied analysis in complex systems. It enables experts to design rapid, efficient, reliable, and high-performance decision ...
GitHub

Keras vs PyTorch: macOS CPU vs GPU Benchmark

This repository provides a comprehensive benchmark comparison of Variational Autoencoder (VAE) implementations for time series anomaly detection. The benchmark evaluates performance across multiple ...
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, ...
Scientific Research Publishing

An, J. and Cho, S. (2015) Variational Autoencoder Based Anomaly Detection Using ...

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 ...
Hacker

An Overview of Log-Based Anomaly Detection Techniques

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?