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A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

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VLAD: Task-agnostic VAE-based lifelong anomaly detection.

Kamil Faber¹, Roberto Corizzo², Bartlomiej Sniezynski¹

¹AGH University of Science and Technology, Institute of Computer Science, Adama Mickiewicza 30, Krakow, 30-059, Poland.

Neural Networks : the Official Journal of the International Neural Network Society

|June 12, 2023

Summary

This summary is machine-generated.

This study introduces VLAD, a novel lifelong anomaly detection method. VLAD effectively detects anomalies in dynamic environments while preserving knowledge, outperforming existing approaches.

Keywords:

Anomaly detection Continual learning Lifelong anomaly detection Lifelong learning Neural networks

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Area of Science:

Machine Learning
Artificial Intelligence

Background:

Lifelong learning is crucial for dynamic environments but under-researched for anomaly detection.
Existing methods fail to balance anomaly detection, adaptation, and knowledge preservation.

Purpose of the Study:

To propose VLAD, a Variational Autoencoder-based Lifelong Anomaly Detection method.
To address challenges in task-agnostic lifelong anomaly detection.

Main Methods:

VLAD combines lifelong change point detection with experience replay and hierarchical memory.
A novel model update strategy supports knowledge consolidation and summarization.

Main Results:

VLAD demonstrates superior performance in lifelong anomaly detection across various settings.
The method shows increased robustness and effectiveness in complex, dynamic environments.

Conclusions:

VLAD successfully addresses the limitations of current methods in lifelong anomaly detection.
The proposed approach offers a robust solution for real-world dynamic anomaly detection scenarios.