Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification-Reference-Cited by-同舟云学术

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Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification

Published:2024-04-14 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Park YeongHyeon¹,Kang Sungho¹,Kim Myung Jin²,Jeong Hyeonho³,Park Hyunkyu¹,Kim Hyeong Seok²,Yi Juneho¹

Affiliation:

1. Sungkyunkwan University,Department of Electrical and Computer Engineering

2. SK Planet Co., Ltd.

3. Sungkyunkwan University,College of Computing

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446942.pdf?arnumber=10446942

Reference20 articles.

1. Visualizing the loss landscape of neural nets;Li;Advances in Neural Information Processing Systems,2018

2. Averaging weights leads to wider optima and better generalization;Izmailov

3. Adversarial pixel restoration as a pretext task for transferable perturbations;Malik

4. GANomaly: Semi-supervised Anomaly Detection via Adversarial Training

5. Anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Visual defect obfuscation based self-supervised anomaly detection;Scientific Reports;2024-08-14

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