Page 28 - Read Online

Page 172 Ji et al. Intell Robot 2021;1(2):151-75 https://dx.doi.org/10.20517/ir.2021.14

23. Murphy K. An introduction to graphical models. Rap tech 2001;96:1-19.
24. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527-54. DOI PubMed
25. Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. Proceedings of the 14th International Conference on Artificial
Intelligence and Statistics (AISTATS); Fort Lauderdale, FL, USA. 2011. p. 315-23.
26. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60:84-
90. DOI
27. Koch G, Zemel R, Salakhutdinov R. Siamese neural networks for one-shot image recognition. Proceedings of the 32nd International
Conference on Machine Learning; Lille, France. 2015.
28. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J,
Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer
International Publishing; 2015. p. 234-41. DOI
29. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In: Kůrková V, Manolopoulos Y, Hammer B,
Iliadis L, Maglogiannis I, editors. Artificial neural networks and machine learning - ICANN 2018. Cham: Springer; 2008. p. 270-9.
30. Goodfellow I, Pouget-abadie J, Mirza M, et al. Generative adversarial networks. Commun ACM 2020;63:139-44. DOI
31. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors.
Computer Vision - ECCV 2014. Cham: Springer International Publishing; 2014. p. 818-33. DOI
32. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of 2015 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR); 2015 Jun 7-12; Boston, MA. IEEE; 2005. p. 1-9. DOI
33. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of 2016 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. p. 770-8. DOI
34. Targ S, Almeida D, Lyman K. Resnet in resnet: generalizing residual architectures. arXiv preprint arXiv:1603.08029.
35. Zhang K, Sun M, Han TX, Yuan X, Guo L, Liu T. Residual networks of residual networks: multilevel residual networks. IEEE Trans
Circuits Syst Video Technol 2018;28:1303-14. DOI
36. Zagoruyko S, Komodakis N. Wide residual networks. arXiv preprint arXiv:1605.07146.
37. Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. Proceedings of 2017 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI, USA. IEEE; 2017. p. 5987-95.
DOI
38. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of 2017 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI, USA. IEEE; 2017. p. 2261-9. DOI
39. Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ. Deep networks with stochastic depth. In: Leibe B, Matas J, Sebe N, Welling M,
editors. Computer vision - ECCV 2016. Cham: Springer International Publishing; 2016. p. 646-61. DOI
40. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
41. He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern
Anal Mach Intell 2015;37:1904-16. DOI PubMed
42. He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification.
Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV); 2015 Dec 7-13; Santiago, Chile. IEEE; 2015. p.
1026-34. DOI
43. Chollet F. Xception: deep learning with depthwise separable convolutions. Proceedings of 2017 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI, USA. IEEE; 2017. p. 1800-7. DOI
44. Howard AG, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint
arXiv:1704.04861.
45. Larsson G, Maire M, Shakhnarovich G. Fractalnet: ultra-deep neural networks without residuals. arXiv preprint arXiv:1605.07648.
46. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y. Overfeat: integrated recognition, localization and detection using
convolutional networks. arXiv preprint arXiv:1312.6229.
47. Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation.
Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition; 2014 Jun 23-28; Columbus, OH, USA. IEEE;
2014. p. 580-7. DOI
48. Girshick R. Fast R-CNN. Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV); 2015 Dec 7-13; Santiago,
Chile. IEEE; 2015. p. 1440-8. DOI
49. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans
Pattern Anal Mach Intell 2017;39:1137-49. DOI PubMed
50. Ouyang W, Zeng X, Wang X, et al. DeepID-Net: deformable deep convolutional neural networks for object detection. IEEE Trans
Pattern Anal Mach Intell 2017;39:1320-34. DOI PubMed
51. Dai J, Li Y, He K, Sun J. R-fcn: object detection via region-based fully convolutional networks. Available from:
https://arxiv.org/pdf/1605.06409.pdf [Last accessed on 5 Jan 2022].
52. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. Proceedings of 2016 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. p. 779-88.
DOI
53. Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector. In: Leibe B, Matas J, Sebe N, Welling M, editors.
Computer vision - ECCV 2016. Cham: Springer International Publishing; 2016. p. 21-37. DOI
54. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell