Abstract
Generally speaking, abnormal images are distinguished from normal images in terms of content or semantics. Image anomaly detection is the task of identifying anomalous images that deviate from normal images. Reconstruction based methods detect anomaly using the difference between the original image and the reconstructed image. These methods assume that the model will be unable to properly reconstruct anomalous images. But in practice, anomalous regions are often reconstructed well due to the network’s generalization ability. Recent methods propose to decrease this effect by turning the generative task to an inpainting problem. By conditioning on the neighborhood of the masked part, small anomalies will not contribute to the reconstrued image. However, it is hard to reconstruct the masked regions when neighborhood exists much anomalous information. We suggest that it should include more useful information of the image when doing inpainting. Inspired by masked autoencoder (MAE), we propose a new anomaly detection method, which called MAE-AD. The architecture of the method can learn global information of the image, and it can avoid being affected by the large anomalous region. We evaluate our method on the MVTec AD dataset, and the results outperform the previous inpainting based approach. In comparison with the methods which use pre-trained models, MAE-AD also has a competitive performance.
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Yu, X., Guo, J., Wang, L. (2023). Image Anomaly Detection and Localization Using Masked Autoencoder. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_33
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