[1] Xu L, Rahmani M, Ma Y X et al. Enhanced light-matter interactions in dielectric nanostructures via machine-learning approach[J]. Advanced Photonics, 2, 026003(2020).

[2] Li D, Jin Y Y, Tong Y et al. Intelligent detection and defect classification of infusion bags based on support vector machine[J]. Laser & Optoelectronics Progress, 56, 131502(2019).

[3] Wang Z R, Fang Y M, Feng H L et al. Method for wooden knot detection and localization[J]. Laser & Optoelectronics Progress, 55, 051501(2018).

[4] Defard T, Setkov A, Loesch A et al. PaDiM: a patch distribution modeling framework for anomaly detection and localization[M]. del Bimbo A, Cucchiara R, Sclaroff S, et al. Pattern recognition. ICPR international workshops and challenges. Lecture notes in computer science, 12664, 475-489(2021).

[5] Burlina P, Joshi N, Wang I J. Where’s wally now? deep generative and discriminative embeddings for novelty detection[C], 11499-11508(2019).

[6] Mei S, Yang H, Yin Z P. An unsupervised-learning-based approach for automated defect inspection on textured surfaces[J]. IEEE Transactions on Instrumentation and Measurement, 67, 1266-1277(2018).

[7] Xue Y J, Chen Q, Zhou S B et al. Research on mechanical abnormal sound detection based on self supervised feature extraction[J]. Laser & Optoelectronics Progress, 59, 1215013(2022).

[8] Schlegl T, Seeböck P, Waldstein S M et al. F-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks[J]. Medical Image Analysis, 54, 30-44(2019).

[9] Schlegl T, Seeböck P, Waldstein S M et al. Unsupervised anomaly detection with generative adversarial networks to Guide marker discovery[M]. Niethammer M, Styner M, Aylward S, et al. Information processing in medical imaging. Lecture notes in computer science, 10265, 146-157(2017).

[10] Bergmann P, Fauser M, Sattlegger D et al. Uninformed students: student-teacher anomaly detection with discriminative latent embeddings[C], 4182-4191(2020).

[11] Salehi M, Sadjadi N, Baselizadeh S et al. Multiresolution knowledge distillation for anomaly detection[C], 14897-14907(2021).

[12] Wang G D, Han S M, Ding E R et al. Student-teacher feature pyramid matching for anomaly detection[EB/OL]. https://arxiv.org/abs/2103.04257

[13] Cohen N, Hoshen Y. Sub-image anomaly detection with deep pyramid correspondences[EB/OL]. https://arxiv.org/abs/2005.02357

[14] Dong D S, Shi K B. Solving the missing cone problem by deep learning[J]. Advanced Photonics, 2, 020501(2020).

[15] Lim J, Ayoub A B, Psaltis D. Three-dimensional tomography of red blood cells using deep learning[J]. Advanced Photonics, 2, 026001(2020).

[16] Chang J, Guan S Q, Shi H Y et al. Strip defect classification based on improved generative adversarial networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 58, 0410016(2021).

[17] Grill J B, Strub F, Altché F et al. Bootstrap your own latent: a new approach to self-supervised learning[EB/OL]. https://arxiv.org/abs/2006.07733

[18] He K M, Fan H Q, Wu Y X et al. Momentum contrast for unsupervised visual representation learning[C], 9726-9735(2020).

[19] Chen T, Kornblith S, Norouzi M et al. A simple framework for contrastive learning of visual representations[C], 1597-1607(2020).

[20] Bergmann P, Batzner K, Fauser M et al. The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection[J]. International Journal of Computer Vision, 129, 1038-1059(2021).

[21] Krizhevsky A, Nair V, Hinton G. Learning multiple layers of features from tiny images[EB/OL]. https://www.semanticscholar.org/paper/Learning-Multiple-Layers-of-Features-from-Tiny-Krizhevsky/5d90f06bb70a0a3dced62413346235c02b1aa086

[22] Coates A, Lee H, Andrew Y. An analysis of single layer networks in unsupervised feature learning[C], 215-223(2011).

[23] Bergmann P, Lowe S, Fauser M et al. Improving unsupervised defect segmentation by applying structural similarity to autoencoders[C], 372-380(2019).