Ibadulla R, Chen TM, Reyes-Aldasoro CC. FatNet: high-resolution kernels for classification using fully convolutional optical neural networks. AI. 2023;4:361–74. https://doi.org/10.3390/ai4020018.

Article  Google Scholar 

Zhou Y, Sun P, Zhang Y, Anguelov D, Gao J, Ouyang T, Guo J, Ngiam J, Vasudevan V. “End-to-end multi-view fusion for 3d object detection in lidar point clouds,” InConference on Robot Learning, 2020, pp. 923–932.

Giv MD, Borujeini MH, Makrani DS, Dastranj L, Yadollahi M, Semyari S, Sadrnia M, Ataei G, Madvar HR. Lung segmentation using active shape model to detect the disease from chest radiography. J Biomed Phys Eng. 2021;11:747.

Google Scholar 

Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks. 2013. arXiv preprint arXiv:1312.6199.

Madry A, Makelov A, Schmidt L, et al. Towards deep learning models resistant to adversarial attacks. 2017. arXiv preprint arXiv:1706.06083.

Kim YG, Kim K, Wu D, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Kim BS, et al. Deep learning-based four-region lung segmentation in chest radiography for COVID-19 diagnosis. Diagnostics. 2022;12:101.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Nguyen A, Yosinski J, Clune J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 427–436.

Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman, Avi Ben Cohen, Nadav Zamir, Asaf Noy, and Lihi Zelnik-Manor. Multi-label classification with partial annotations using class aware selective loss. In Proceedings of the IEEE/CVF Con ference on Computer Vision and Pattern Recognition, pages 4764–4772, 2022.

Juncheng Li, Siliang Tang, Linchao Zhu, Wenqiao Zhang, Yi Yang, Tat-Seng Chua, and Fei Wu. Variational cross graph reasoning and adaptive structured semantics learning for compositional temporal grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

Cavalin P, Oliveira L. Confusion matrix-based building of hierarchical classification[C]//Iberoamerican Congress on Pattern Recognition. Cham: Springer; 2018. p. 271–8.

Google Scholar 

Law H, Deng J. CornerNet: detecting objects as paired keypoints. In Proceedings of the European conference on computer vision (ECCV). 2048;734–750.

LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks. 1995;3361(10):1995.

Google Scholar 

Biederman I. Recognition-by-components: a theory of human image understanding. Psychol Rev. 1987;94(2):115–47.

Article  PubMed  Google Scholar 

Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA. 1982;79:2554–8.

Article  ADS  MathSciNet  PubMed  PubMed Central  CAS  Google Scholar 

Yu X, Johal S, Geng J. Visual search guidance uses coarser template information than target-match decisions. Atten Percept Psychophys. 2022;84(5):1432–45.

Article  PubMed  PubMed Central  Google Scholar 

Lau J, Pashler H, Brady T. Target templates in low target-distractor discriminability visual search have higher resolution, but the advantage they provide is short-lived. Atten Percept Psychophys. 2021;83(4):1435–54.

Article  PubMed  PubMed Central  Google Scholar 

Kiat J, Bahle B, Luck S. Search templates for real-world objects in natural scenes. J Vis. 2022;22(14):4477.

Article  Google Scholar 

Volkova S. Template selection technique on object recognition. Proc. SPIE 12564, International Conference on Computer Applications for Management and Sustainable Development of Production and Industry. 2023;125640V.

Sahadevan S, Chen Y, Caplan J. Imagery-based strategies for memory for associations. Memory. 2021;29(10):1275–95.

Article  PubMed  Google Scholar 

Mei L, Zhao Y, Wang H, Wang C, Zhang J, Zhao X. Matching by pixel distribution comparison: multisource image template matching. IET Signal Process. 2022;17(2).

Le M, Lien J. Robot arm grasping using learning-based template matching and self-rotation learning network. Preprint of Research Square. 2022. https://doi.org/10.21203/rs.3.rs-1402918/v1.

Li D, Song L, Wei Q, Chai H, Han T. Dynamic learning rate of template update for visual target tracking. Mathematics. 2023;11(9):1988.

Article  Google Scholar 

Hanne A, Tünnermann J, Schubö A. Target templates and the time course of distractor location learning. PsyArXiv. 2022. https://doi.org/10.31234/osf.io/728ch

Liu T, Wei B, Chang B, Sui Z. Large-scale simple question generation by template-based Seq2seq learning. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural language processing and Chinese computing. NLPCC 2017. Lect Notes Comput Sci. 2018;10619. Springer, Cham.

Wei H, Pan S, Ma G, Duan X. Vision-guided hand–eye coordination for robotic grasping and its application in tangram puzzles. AI 2021, 2, 209–228. https://doi.org/10.3390/ai2020013.

Wei H, Li H. Shape description and recognition method inspired by the primary visual cortex. Cogn Comput. 2014;6:164–74.

Article  Google Scholar 

Alain G, Bengio Y. Understanding intermediate layers using linear classifier probes. 2016. arXiv preprint arXiv:1610.01644.

LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proc IEEE. 1998;86(11):2278–324.

Article  Google Scholar 

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2016:770–8.

Google Scholar 

Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017;2017:2261–9.

Google Scholar 

Kabir HM, Abdar M, Jalali SMJ, et al. SpinalNet: deep neural network with gradual input. arXiv preprint arXiv:2007.03347, 2020.

Jayasundara V, Jayasekara S, Jayasekara H, et al. TextCaps: handwritten character recognition with very small datasets[C]//2019 IEEE winter conference on applications of computer vision (WACV). IEEE, 2019: 254–262.

Howard AG. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017. https://doi.org/10.48550/arXiv.1704.04861.

Ma N, Zhang X, Zheng H-T, Sun J. ShuffleNet V2: practical guidelines for efficient CNN architecture design. 2018. https://doi.org/10.48550/arXiv.1807.11164.

Cohen G, Afshar S, Tapson J, et al. EMNIST: extending MNIST to handwritten letters[C]//2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017: 2921–2926.

Dufourq E, Bassett BA. Eden: Evolutionary deep networks for efficient machine learning[C]//2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech). IEEE. 2017:110–115.

Cheolhwan O, Zak SH. Large-scale pattern storage and retrieval using generalized brain-state-in box neural networks. IEEE Trans Neural Networks. 2010;4(21):633–43.

Google Scholar 

Kosko B. Adaptive bidirectional associative memories. Appl Opt. 1987;26(23):4947–4860.

Article  ADS  PubMed  CAS  Google Scholar 

Kosko B. Constructing an associative memory. Byte. 1987;12(10):137–44.

Google Scholar 

Kosko B. Bidirectional associative memory. IEEE Trans Syst Man Cybern. 1988;18(1):49–60.

Article  MathSciNet  Google Scholar 

Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks. InProceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1125–1134.

Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. 2017. arXiv preprint arXiv:1706.03762.

Ronneberger O, Fischer P, Brox TT. U-Net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer: Cham; 2015. p. 234–41.

Google Scholar 

Wang Z, Cun X, Bao J, Zhou W, Liu J, Li H. Uformer: a general U-shaped transformer for image restoration. In CVPR. 2022;6.

Kramer MA. Nonlinear principal component analysis using autoassociative neural networks[J]. AIChE J. 1991;37(2):233–43.

Article  ADS  CAS  Google Scholar 

Lu X, Tsao Y, Matsuda S, et al. Speech enhancement based on deep denoising autoencoder[C]//Interspeech. 2013, 2013: 436–440.

Makhzani A, Frey B. K-sparse autoencoders. 2013. arXiv preprint arXiv:1312.5663.

An J, Cho S. Variational autoencoder based anomaly detection using reconstruction probability[J]. Special Lecture on IE. 2015;2(1):1–18.

Google Scholar 

Zhang L, Chen X, Tu X, Wan P, Xu N, Ma K. Wavelet knowledge distillation: towards efficient image-to-image translation. In CVPR. 2022;6.

Goodfellow IJ. “Generative adversarial networks”, arXiv e-prints, 2014. https://doi.org/10.48550/arXiv.1406.2661.

Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. UNet++: A nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. DLMIA ML-CDS 2018. Lect Notes Comput Sci. 2018;11045. Springer, Cham. https://doi.org/10.1007/978-3-030-00889-5_1.

Cohen G, Afshar S, Tapson J,  Van Schaik A. EMNIST: an extension of MNIST to handwritten letters. 2017. Retrieved from arxiv.org/abs/1702.05373.