The generative adversarial networks and its application in machine vision

References

• Amraei, S., S. Abdanan Mehdizadeh, and S. Salari. 2017. “Broiler Weight Estimation Based on Machine Vision and Artificial Neural Network.” British Poultry Science 58 (2): 200–205. doi:https://doi.org/10.1080/00071668.2016.1259530.
   Google Scholar
• Bousmalis, K, N Silberman, and D Dohan, et al. “Unsupervised Pixel-level Domain Adaptation with Generative Adversarial Networks” Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. 2017: 3722–3731,  Seattle, Washington.
   Google Scholar
• Brock, A., J. Donahue, and J. Simonyan. 2019. “Large Scale Gan Training For High Fidelity Natural Image Synthesis.” International Conference on Learning Representations (ICLR),  Ernest N. Morial Convention Center, New Orleans.
   Google Scholar
• Chen, C.-M., B. Xiang, Y. Liu, and K.-H. Wang. 2019a. “A Secure Authentication Protocol for Internet of Vehicles.” IEEE Access 7 (1): 12047–12057. doi:https://doi.org/10.1109/ACCESS.2019.2891105.
   Google Scholar
• Chen, C.-M., K.-H. Wang, K.-H. Yeh, B. Xiang, and W. Tsu-Yang. 2019b, August. “Attacks and Solutions on a Three-party Password-based Authenticated Key Exchange Protocol for Wireless Communications.” Journal of Ambient Intelligence and Humanized Computing 10 (8): 3133–3142. doi:https://doi.org/10.1007/s12652-018-1029-3.
   Google Scholar
• Chen, X., A. Li, X. Zeng, W. Guo*, and G. Huang. 2015. “Runtime Model Based Approach to IoT Application Development.” Frontiers of Computer Science 9 (4): 540–553.
   Google Scholar
• Chen, X., Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel. 2016. “Info Gan: Interpretable Representation Learning By Information Maximizing Generative Adversarial Nets.” Advances Neural Information Processing Systems Conference, 2172–2180, Barcelona, Spain.
   Google Scholar
• Cheng, Y., H. Jiang, F. Wang, Y. Hua, D. Feng, W. Guo, and Y. Wu. 2019. “Using High-Bandwidth Networks Efficiently for Fast Graph Computation.” IEEE Transactions on Parallel and Distributed Systems 30 (5): 1170–1183. doi:https://doi.org/10.1109/TPDS.71.
   Google Scholar
• Dai, H. N., H. Wang, G. Xu, J. Wan, and M. Imran. 2019. “Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies.” Enterprise Information Systems 1–25. doi:https://doi.org/10.1080/17517575.2019.1633689
   Google Scholar
• Demir, U., and G. Unal. 2018. “Patch-Based Image Inpainting with Generative Adversarial Networks.”
   Google Scholar
• Denton, E. L., S. Chintala, and A. Szlam. 2015. “Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks.” Advances Neural Information Processing Systems Conference, 1486–1494, Montreal, Quebec, Canada.
   Google Scholar
• Dosovitskiy, A., and T. Brox. 2016. “Generating Images with Perceptual Similarity Metrics Based on Deep Networks.”
   Google Scholar
• Esmaeilian, B., S. Behdad, and B. Wang. 2016. “The Evolution and Future of Manufacturing: A review.” Journal of Manufacturing Systems 39: 79–100. doi:https://doi.org/10.1016/j.jmsy.2016.03.001.
   Google Scholar
• Ganin, Y., E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky. 2016. “Domain Adversarial Training of Neural Network.” Journal of Machine Learning Research 17 (59): 1–35.
   Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio. 2014. “Generative Adversarial Nets.” Advances Neural Information Processing Systems Conference, 2672–2680, Montreal, Quebec, Canada.
   Google Scholar
• Guo, K., W. Guo*, Y. Chen, Q. Qiu, and Q. Zhang. 2015a. “Community Discovery by Propagating Local and Global Information Based on the MapReduce Model.” Information Sciences 323: 73–93. doi:https://doi.org/10.1016/j.ins.2015.06.032.
   Google Scholar
• Guo, W., and G. Chen*. 2015. “Human Action Recognition via Multi-task Learning Base on Spatial-temporal Feature.” Information Sciences 320: 418–428. doi:https://doi.org/10.1016/j.ins.2015.04.034.
   Google Scholar
• Guo, W., J. Li, G. Chen*, Y. Niu, and C. Chen. 2015b. “A PSO-Optimized Real-time Fault-tolerant Task Allocation Algorithm in Wireless Sensor Networks.” IEEE Transactions on Parallel and Distributed Systems 26 (12): 3236–3249. doi:https://doi.org/10.1109/TPDS.2014.2386343.
   Google Scholar
• Harding, J. A., M. Shahbaz, and A. Srinivas Kusiak. 2006. “Data Mining in Manufacturing: A Review.” Journal of Manufacturing Science and Engineering 128: 969–976. doi:https://doi.org/10.1115/1.2194554.
   Google Scholar
• Hazen, B. T., C. A. Boone, J. D. Ezell, and L. A. Jones-Farmer. 2014. “Data Quality for Data Science,predictive Analytics, and Big Data in Supply Chain Management: An Introduction to the Problem and Suggestions for Research and Applications.” International Journal of Production Economics 154 (4): 72–80. doi:https://doi.org/10.1016/j.ijpe.2014.04.018.
   Google Scholar
• Huang, X., W. Guo*, G. Liu, and G. Chen. 2016. “FH-OAOS:A Fast 4-step Heuristic for Obstacle-Avoiding Octilinear Architecture Router Construction.” ACM Transactions on Design Automation of Electronic System 21 (3): 30 pages. Article 48. doi:https://doi.org/10.1145/2856033.
   Google Scholar
• Iizuka, S., E. Simo-Serra, and H. Ishikawa. 2017. “Globally And Locally Consistent Image Completion.” Acm Transactions On Graphics (Tog) 36 (4): 107. doi:https://doi.org/10.1145/3072959.
   Google Scholar
• Kang, H. S., Y. L. Ju, S. S. Choi, H. Kim, and J. H. Park. 2016. “Smart Manufacturing: Past Research,present Findings, and Future Directions.” International Journal of Precision Engineering and Manufacturing – Green Technology 3 (1): 111–128. doi:https://doi.org/10.1007/s40684-016-0015-5.
   Google Scholar
• Ledig, C., L. Theis, F. Huszar, et al. 2016. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681–4690.
   Google Scholar
• Lee, J. D., M. Simchowitz, M. I. Jordan, B. Recht. 2016. “Gradient Descent Only Converges To Minimizers.” Proceedings of Conference on Learning Theory, 1246–1257, Columbia University, New-York City, USA.
   Google Scholar
• Lin, B., W. Guo *, N. Xiong*, G. Chen, A. V. Vasilakos, and H. Zhang. 2016. “A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multi-cloud Environments.” IEEE Transactions on Network and Service Management 13 (3): 581–594. doi:https://doi.org/10.1109/TNSM.2016.2554143.
   Google Scholar
• Liu, G., W. Guo*, Y. Niu, G. Chen, and X. Huang. 2015a. “A PSO-based-timing-driven Octilinear Steiner Tree Algorithm for VLSI Routing considering Bend Reduction.” Soft Computing 19 (5): 1153–1169. doi:https://doi.org/10.1007/s00500-014-1329-2.
   Google Scholar
• Liu, G., X. Huang, W. Guo*, Y. Niu, and G. Chen. 2015b. “Multilayer Obstacle-Avoiding X-Architecture Steiner Minimal Tree Construction Based on Particle Swarm Optimization.” IEEE Transactions on Cybernetics 45 (5): 989–1002. doi:https://doi.org/10.1109/TCYB.2014.2342713.
   Google Scholar
• Luo, F., W. Guo*, Y. Yu, and G. Chen. 2016. “A Multi-label Classification Algorithm Based on Kernel Extreme Learning Machine.” Neurocomputing 260: 313–320. doi:https://doi.org/10.1016/j.neucom.2017.04.052.
   Google Scholar
• Metz, L., B. Poole, D. Pfau, et al. 2016. “Unrolled Generative Adversarial Networks.” (2016)[J]. arXiv preprint arXiv:1611.02163.
   Google Scholar
• Mirza, M., and S. Osindero. 2019. “Conditional Generative Adversarial Nets [Eb/Ol].” March, 16. Https: Arxiv.Org/pdf/1411.1784.pdf
   Google Scholar
• Mo, Y. L. Xing, Y. K. Lin, W. Guo 2018. “Efficient Analysis of Repairable Computing Systems Subject to Scheduled Checkpointing.”IEEE Transactions on Dependable and Secure Computing 1. doi:https://doi.org/10.1109/TDSC.2018.2869393.
   Google Scholar
• Ng, C. K., C. H. Wu, K. L. Yung, W. H. Ip, and T. Cheung. 2018. “A Semantic Similarity Analysis of Internet of Things.” Enterprise Information Systems 12 (7): 820–855. doi:https://doi.org/10.1080/17517575.2018.1464666.
   Google Scholar
• Niu, Y., J. Chen, and W. Guo*. 2018. “Meta-Metric for Saliency Detection Evaluation Metrics Based on Application Preference.” Multimedia Tools and Applications 77 (20): 26351–26369. Online Publication. doi:https://doi.org/10.1007/s11042-018-5863-2.
   Google Scholar
• Nowozin, S., B. Cseke, and R. Tomioka. 2016. “F-Gan: Training Generative Neural Samplers Using Variational Divergence Minimization.” Advances Neural Information Processing Systems Conference, 271–279, Barcelona, Spain.
   Google Scholar
• Pan, J.-S., C.-Y. Lee, A. Sghaier, M. Zeghid, and J. Xie. 2019. “Novel Systolization of Subquadratic Space Complexity Multipliers Based on Toeplitz Matrix–Vector Product Approach.” IEEE Transactions on Very Large Scale Integration Systems 27 (7): 1614–1622. doi:https://doi.org/10.1109/TVLSI.2019.2903289.
   Google Scholar
• Pathak, D., P. Krahenbuhl, J. Donahue, T. Darrell, A. A. Efros. 2016. “Context Encoders: Feature Learning By Inpainting.” Proceedings of The IEEE Conference on Computer Vision And Pattern Recognition, Las Vegas, NV, USA, 2536–2544.
   Google Scholar
• Pei Breivold, H. 2019. “Towards Factories of the Future: Migration of Industrial Legacy Automation Systems in the Cloud Computing and Internet-of-things Context.” Enterprise Information Systems 1–21. doi:https://doi.org/10.1080/17517575.2018.1556814.
   Google Scholar
• Radford, A., L. Metz, and S. Chintala. 2016. “Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks.” International Conference on Learning Representations Workshop Track, Caribe Hilton, San Juan.
   Google Scholar
• Salimans, T., I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen. 2016. “Improved Techniques For Training Gans.” Advances Neural Information Processing Systems Conference, 2226–2234, Las Vegas, NV, USA.
   Google Scholar
• Shin, S. J., J. Woo, and S. Rachuri. 2014. “Predictive Analytics Model for Power Consumption in Manufacturing.” Procedia CIRP 15: 153–158. doi:https://doi.org/10.1016/j.procir.2014.06.036.
   Google Scholar
• Shrivastava, A., T. Pfister, O. Tuzel, et al. 2016. “Learning From Simulated And Unsupervised Images Through Adversarial Training.” Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR), 2107–2116.
   Google Scholar
• Theis, L., A. Oord, and M. Bethge. 2018. “A Note on The Evaluation Of Generative Models.” International Conference on Learning Representations (ICLR), Vancouver Convention Center,Vancouver, BC, Canada.
   Google Scholar
• Tobias, J. 2016. “Unsupervised And Semi-Supervised Learning With Categorical Generative Adversarial Network.” Iclr-2016, 876–884. Springer.
   Google Scholar
• Tsu-Yang, W., C.-M. Chen, K.-H. Wang, C. Meng, and E. K. Wang. 2019. “A Provably Secure Certificateless Public Key Encryption with Keyword Search.” Journal of the Chinese Institute of Engineers 42 (1): 20–28. doi:https://doi.org/10.1080/02533839.2018.1537807.
   Google Scholar
• Wang, J., Y. Ma, L. Zhang, R. X Gao, D. Wu 2018. “Deep Learning for Smart Manufacturing: Methods and Applications.” Journal of Manufacturing Systems 48: 144–156. doi:https://doi.org/10.1016/j.jmsy.2018.01.003.
   Google Scholar
• Wang, S., and W. Guo*. 2017. “Robust Co-clustering via Dual Local Learning and High-order Matrix Factorization.” Knowledge-Based Systems 138: 176–187. doi:https://doi.org/10.1016/j.knosys.2017.09.033.
   Google Scholar
• Yeh, R. A., C. Chen, T. Y. Lim, et al. 2017. “Semantic Image Inpainting With Deep Generative Models.” Proceedings of The 2017 IEEE Conference on Computer Vision and Pattern Recognition, 6882–6890, Honolulu, HI, USA.
   Google Scholar
• Zhang, M., K. T. Ma, J. H. Lim, Q. Zhao, J. Feng. 2017. “Deep Future Gaze: Gaze Anticipation On Egocentric Videos Using Adversarialnetworks.” IEEE Conference on Computer Vision Pattern Recognition (Cvpr), 4372–4381, Honolulu, HI, USA.
   Google Scholar
• Zhou, B., A. Khosla, A. Lapedriza, A. Oliva, A. Torralba. 2016. “Learning Deep Features For Discriminative Localization.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929,  Las Vegas, NV, USA.
   Google Scholar
• Zhu, J. Y., T. Park, P. Isola, A. A. Efros. 2017. “Unpaired Image-To-Image Translation Using Cycle Consistent Adversarial Networks.” IEEE International Conference on Computer Vision (ICCV), 2242–2251, Venice, Italy.
   Google Scholar
• Zhu, L., Y. Chen, P. Ghamisi, J. A. Benediktsson. 2018a. “Generative Adversarial Networks For Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (9): 5046–5063. doi:https://doi.org/10.1109/TGRS.2018.2805286.
   Google Scholar
• Zhu, W., W. Guo*, Z. Yu, and H. Xiong. 2018b. “Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing.” Wireless Communications and Mobile Computing, 10 pages, Article 7218061.
   Google Scholar