Work

Improved Anomaly Detection Using Efficient GAN and Mapping Consistency

Year: 2025

Type: article

Abstract: Anomaly detection is essential in a wide range of fields. In this study, we focus on an Efficient GAN applied to anomaly detection, and aim to improve its performance by random erasing data augmentation and enhancing the loss function to incorporate mapping consistency. Experiments using images of normal lemons and damaged lemons reveal that the proposed method significantly improves the anomaly detection performance of Efficient GAN.

Source: IEICE Transactions on Information and Systems

DOI: 10.1587/transinf.2024edl8105

Authors: Rihito SHODA, Seiji MIYOSHI

Institution: Kansai University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Japan Society for the Promotion of Science

Award: New developments in signal statistical mechanics for nonlinear system analysis