Create New Account
Login
Search or Buy Articles
Browse Journals
Browse Proceedings
Submit your Paper
Submission Information
Journal Review
Recommend to Your Library
Call for Papers
AUTOMATED DEFECT DETECTION BASED ON TRANSFER LEARNING AND DEEP CONVOLUTION GENERATIVE ADVERSARIAL NETWORKS, 471-478.
Yangbo Feng, Tinglong Tang, Shengyong Chen, and Yirong Wu
References
[1] E. Mendez, G.M. Mafla, and F. Reyes, Analysis, review anddevelopment of a conceptual model, based on class diagramsas a component of UML, focused on industrial automation.International Journal of Robotics and Automation, 4(1), 2019,6–10.
[2] B. Erik, M. Iman, H.E. Carl, and D.F.C. Neill, Nonparametricinference for auto-encoding variational bayes, arXiv e-prints,2017, vol. 1712.06536.
[3] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A.Radford, and X. Chen, Improved techniques for training GANs,In Advances in Neural Information Processing Systems, CurranAssociates, 2016, 2234–2242.
[4] T. Eric, H. Judy, S. Kate, and D. Trevor, Adversarial discrim-inative domain adaptation, IEEE Conference on ComputerVision and Pattern Recognition (CVPR), 2017, 2962–2971.
[5] S.H. Khan, M. Hayat, M. Bennamoun, F.A. Sohel, and R.Togneri. Cost-sensitive learning of deep feature representationsfrom imbalanced data, IEEE Transactions on Neural Networks& Learning Systems, 29(8), 2018, 3573–3587.
[6] Y.L. Tsung, G. Priya, G. Ross, K.M. He, and D. Piotr, Focalloss for dense object detection, IEEE Transactions on PatternAnalysis & Machine Intelligence, 99, 2017, 2999–3007.
[7] A.S. Vishwanath and S. Sumit, Domain adaptation for au-tomatic OLED panel defect detection using adaptive supportvector data description, International Journal of ComputerVision, 122(2), 2017, 193–211.
[8] A. Newson, A. Almansa, Y. Gousseau, and P. P´erez. Robustautomatic line scratch detection in films. IEEE Transactionson Image Processing, 23(3), 2014, 1240–1254.
[9] T.M.T. Nhat and K. Sanghoon, Automatic image thresholdingusing Otsu’s method and entropy weighting scheme for surfacedefect detection, Soft Computing, 22(13), 2018, 4197–4203.
[10] Z. Xiao, M. Wang, L. Geng, J. Wu, F. Zhang, and C.Shan, Optic cup segmentation method by a modified VGG-16network, Journal of Medical Imaging and Health Informatics,9(1), 2019, 97–101.
[11] S. Targ, D. Almeida, and K. Lyman, Resnet in resnet:Generalizing residual architecture, eprint arXiv:1603.08029,2016.
[12] I. J. Goodfellow, J. Abadie, M. Mirza, X. Bing, D.W. Farley, S.Ozair, A. Courville, and Y. Bengio, Generative adversarialnets,International Conference on Neural Information ProcessingSystems, Montr´eal Canada, 2014, 2672–2680.
[13] A. Radford, L. Metz, and S. Chintala, Unsupervised rep-resentation learning with deep convolutional generative ad-versarial networks, Computer Science, arXiv: 1511.06434,2015.
[14] Y. Yang, Z. Gong, Z. Ping, and J. Shan, Unsupervised repre-sentation learning with deep convolutional neural network forremote sensing images, Image and Graphics, ICIG, LectureNotes in Computer Science, Shanghai, China, 2017, 10667.
[15] A. Kitchen and J. Seah, Deep generative adversarial neuralnetworks for realistic prostate lesion MRI synthesis, arXiv,CoRR, 2018, vol. abs/1708.00129.
[16] M. Mardani, E. Gong, J.Y. Cheng, S.S. Vasanawala, andJ.M. Pauly, Deep generative adversarial neural networks forcompressive sensing (GANCS) MRI, IEEE Transactions onMedical Imaging, 38(1), 2018, 167–179.
[17] A. Wulffff-Jensen, N.N. Rant, T.N. Miller, and J.A. Billeskov,Deep convolutional generative adversarial network for proce-dural 3D landscape generation based on DEM, Interactivity,Game Creation, Design, Learning, and Innovation, Cham:Springer, 2018, 85–94.
[18] J. Zhang and Z. Shi, Deformable deep convolutional gener-ative adversarial network in microwave based hand gesturerecognition system, arXive-prints, 2017, vol. 1711.01968.
[19] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, andH. Lee, Generative adversarial text to image synthesis, arXive-prints, 2016, vol. 1605.05396.
[20] M. Fabbri, S. Calderara, and R. Cucchiara, Generative adver-sarial models for people attribute recognition in surveillance,IEEE International Conference on Advanced Video & SignalBased Surveillance, Macao, China, 2017, 1–6.
[21] L. Horsley and D. Perez-Liebana, Building an automatic spritegenerator with deep convolutional generative adversarial net-works, IEEE Conference on Computational Intelligence andGames (CIG), IEEE, New York, NY, USA, 2017.
[22] C. Baur, S. Albarqouni, and N. Navab, MelanoGANs: Highresolution skin lesion synthesis with GANs, arXiv, 2018, vol.1804.04338.477
[23] X. Zhang, J. Zou, K. He, and J. Sun, Accelerating verydeep convolutional networks for classification and detection,Computer Science, 2015, arXiv preprint: 1505.06798.
[24] K. JiwonL.J. Kwon, and L.K.M. Jiwon, Accurate image super-resolution using very deep convolutional networks, IEEE Con-ference on Computer Vision and Pattern Recognition (CVPR),2016, Las Vegas, NV, USA, 1646–1654.
[25] Y.M. Qian and P.C. Woodland, Very deep convolutional neuralnetworks for robust speech recognition, IEEE Spoken LanguageTechnology Workshop, San Juan, USA, 2016, 481–488.
[26] S. Liu and W. Deng, Very deep convolutional neural networkbased image classification using small training sample size,IEEE IAPR Asian Conference on Pattern Recognition (ACPR),2016, 730–734.
[27] R. Paul, Classifying cooking object’s state using a tunedVGG convolutional neural network, CoRR, 2018, vol.abs/1805.09391.
[28] J. Miller, U. Nair, R. Ramachandran, and M. Maskey, Detectionof transverse cirrus bands in satellite imagery using deeplearning, Computers & Geosciences, 118, 2018, 79–85.
[29] S.T. Hang and M. Aono, Open world plant image identificationbased on convolutional neural network, Signal & InformationProcessing Association Summit & Conference, IEEE, Lumpur,Malaysia, 2017, vol. 16602949.
[30] Z. Meng, X. Fan,C. Xin, C. Min, and T. Yan, Detectingsmall signs from large images, IEEE International Conferenceon Information Reuse and Integration (IRI), San Diego, CA,USA, 2017, 217–224.
[31] T. Akilan, J. Wu, and H. Zhang, Effect of fusing featuresfrom multiple DCNN architectures in image classification, IETImage Processing, 12(7), 2018, 1102–1110.
[32] Z. Zheng, L. Zheng, and Y. Yang, Unlabeled samples generatedby GAN improve the person re-identification baseline in vitro,IEEE International Conference on Computer Vision (ICCV),IEEE Computer Society, Venice, Italy, 2017, 3774–3782.
[33] G. Huang, Z. Liu, V.D.M. Laurens, and K.Q. Weinberger,Densely connected convolutional networks, IEEE Conferenceon Computer Vision and Pattern Recognition (CVPR), Hawaii,USA, 2017, 2261–2269.
[34] K.M. He, X.Y. Zhang, S.Q. Ren, and J. Sun, Deep residuallearning for image recognition, IEEE Conference on ComputerVision and Pattern Recognition (CVPR), Las Vegas, NV, USA,2016, 770–778.
[35] K. Simonyan and A. Zisserman, Very deep convolutional net-works for large-scale image recognition, Computer Science,2014, arXiv preprint: 1409.1556.
Important Links:
Abstract
DOI:
10.2316/J.2021.206-0735
From Journal
(206) International Journal of Robotics and Automation - 2021
Go Back