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Anomaly detection in images with shared autoencoders.

Haoyang Jia^1,2, Wenfen Liu^1,2

¹Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, China.

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|January 23, 2023

Summary

This summary is machine-generated.

This study introduces a novel encoder-decoder-encoder (EDE) model for anomaly detection in imbalanced computer vision datasets. The EDE model effectively distinguishes normal from abnormal data using a unique two-stage training approach.

Keywords:

adversarial network anomaly detection autoencoder (AE)image identification unsupervised learning

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Anomaly detection is challenging in computer vision due to imbalanced datasets where normal data significantly outweighs abnormal data.
Traditional methods often struggle with insufficient samples of abnormal instances, hindering accurate identification.

Purpose of the Study:

To introduce a novel Encoder-Decoder-Encoder (EDE) model for robust anomaly detection.
To address the limitations of existing methods in handling highly biased datasets.

Main Methods:

A novel Encoder-Decoder-Encoder (EDE) architecture with two decoders and two shared encoders is proposed.
A unique two-stage training strategy is employed, combining autoencoder-like reconstruction loss with generative adversarial principles.

Main Results:

The proposed EDE model achieves state-of-the-art performance on several public image datasets.
The two-stage training effectively leverages generative confrontation to improve anomaly detection accuracy.

Conclusions:

The EDE model offers a significant advancement in anomaly detection for imbalanced computer vision datasets.
This approach overcomes limitations of traditional Autoencoders (AEs) and Generative Adversarial Networks (GANs).