Anomaly detection algorithm for asymmetric autoencoder based on knowledge distillation

Jianqin Liu; Buwei Wu; Li Yu

doi:10.1117/12.3029703

13 May 2024 Anomaly detection algorithm for asymmetric autoencoder based on knowledge distillation

Jianqin Liu, Buwei Wu, Li Yu

Proceedings Volume 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024); 1315807 (2024) https://doi.org/10.1117/12.3029703
Event: Seventh International Conference on Computer Graphics and Virtuality (ICCGV24), 2024, Hangzhou, China

Abstract

Anomaly detection is a research hotspot in the field of object detection, aiming to construct models using normal samples to detect anomalies. The challenge of this task is the extreme imbalance of the dataset, and training models based on such datasets do not have good generalization ability. In order to solve the problem of low abnormal data affecting detection performance, we propose an asymmetric self-coding network based on knowledge distillation, combined with anomaly detection algorithms. Our method only uses normal samples for training, allowing the encoder to learn the distribution of normal samples in deep space. We use the decoder to reconstruct and restore deep features, outputting a generated graph of the corresponding samples. By forming an asymmetric structure with a lightweight decoder and encoder, the problem of reconstruction error failure is solved. The knowledge distillation algorithm is combined to train the network, using the pretrained encoding network as the teacher network and guiding the reconstruction of the asymmetric decoding network as the student network. A new multi-scale loss function is designed, which is composed of pixel level and global direction loss function. Experiments show that the average AUC of each category of MVTec AD dataset in our method is significantly higher than other anomaly detection methods. Especially when knowledge distillation strategy is used in reconstruction methods, the average AUC of our method is about 2 points higher than the highest MKD network.

Citation Download Citation

Jianqin Liu, Buwei Wu, and Li Yu "Anomaly detection algorithm for asymmetric autoencoder based on knowledge distillation", Proc. SPIE 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024), 1315807 (13 May 2024); https://doi.org/10.1117/12.3029703

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KEYWORDS

Image restoration

Object detection

Reconstruction algorithms

Design

Feature extraction

Overfitting

X-ray imaging

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