ADDP : Anomaly Detection Based on Denoising Pretraining

• Autorzy: Ge Xianlei , Li Xiaoyan , Zhang Zhipeng

Identyfikatory

DOI

10.24425/ijet.2023.147693

• Języki publikacji: EN

Abstrakty

EN

Acquiring labels in anomaly detection tasks is expensive and challenging. Therefore, as an effective way to improve efficiency, pretraining is widely used in anomaly detection models， which enriches the model's representation capabilities, thereby enhancing both performance and efficiency in anomaly detection. In most pretraining methods, the decoder is typically randomly initialized. Drawing inspiration from the diffusion model, this paper proposed to use denoising as a task to pretrain the decoder in anomaly detection, which is trained to reconstruct the original noise-free input. Denoising requires the model to learn the structure, patterns, and related features of the data, particularly when training samples are limited. This paper explored two approaches on anomaly detection: simultaneous denoising pretraining for encoder and decoder, denoising pretraining for only decoder. Experimental results demonstrate the effectiveness of this method on improving model’s performance. Particularly, when the number of samples is limited, the improvement is more pronounced.

Słowa kluczowe

EN

Anomaly Detection; Diffusion Models; image denoising; Pretraining; transfer learning

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2023
• Tom: Vol. 69, No. 4
• Strony: 719--726
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Ge Xianlei

• School of Electronic Engineering, Huainan Normal University, China; College of Computing and Information Technologies, National University, Philippines

autor

Li Xiaoyan

• School of Computer, Huainan Normal University, China

autor

Zhang Zhipeng

• School of Electronic Engineering, Huainan Normal University, China

Bibliografia

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Uwagi

1. Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

2. This work was supported by the University Natural Science Foundation of Anhui Province (Grant No.2022AH051578,2023AH051551), Key Science Research Foundation of Huainan Normal University (Grant No.2022XJZD019) and Guiding Science and Technology Foundation of Huainan (Grant No.2020050).