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1Report
المؤلفون: 周宗南, Chou, Tsung-Nan
المساهمون: 財務金融系
وصف الملف: 887438 bytes; application/pdf
Relation: http://ir.lib.cyut.edu.tw:8080/handle/310901800/42151; http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/42151/2/MOE-111年教學實踐研究計畫成果報告-周宗南-Upload.pdf
الاتاحة: http://ir.lib.cyut.edu.tw:8080/handle/310901800/42151
http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/42151/2/MOE-111年教學實踐研究計畫成果報告-周宗南-Upload.pdf -
2Dissertation/ Thesis
المؤلفون: 莊格維, Chuang, Ko-Wei
المساهمون: 李蔡彥 黃瀚萱, Tsai-Yen Li Hen-Hsen Huang
مصطلحات موضوعية: 雜訊標籤學習, 群眾學習, 對抗意識攻擊, 對抗訓練, Noisy label learning, Learning from Crowdsourcing, Adversarial-awareness-attack, Adversarial training
وصف الملف: 1622150 bytes; application/pdf
Relation: G0109971002; https://nccur.lib.nccu.edu.tw//handle/140.119/153280; https://nccur.lib.nccu.edu.tw/bitstream/140.119/153280/1/100201.pdf
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3Dissertation/ Thesis
المؤلفون: 胡育騏, Hu, Yu-Chi
المساهمون: 郁方, Yu, Fang
مصطلحات موضوعية: 抽象解釋, 對抗訓練, 邏輯規則, 模型穩健性, Abstract interpretation, Adversarial training, Logic rule, Robustness
وصف الملف: 4041611 bytes; application/pdf
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Nebot, “Automated evaluation of semantic segmentation robustness for autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 5, pp. 1951–1963, 2019.\n[6] F. Tramer and D. Boneh, “Adversarial training and robustness for multiple perturbations,” Advances in neural information processing systems, vol. 32, 2019.\n[7] S. Zheng, Y. Song, T. Leung, and I. Goodfellow, “Improving the robustness of deep neural networks via stability training,” in Proceedings of the ieee conference on computer vision and pattern recognition, 2016, pp. 4480–4488.\n[8] D. Zügner and S. Günnemann, “Certifiable robustness and robust training for graph convolutional networks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 246–256.\n[9] C. Xie, M. Tan, B. Gong, J. Wang, A. L. Yuille, and Q. V. Le, “Adversarial examples improve image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 819–828.\n[10] A. Aldahdooh, W. Hamidouche, S. A. Fezza, and O. Déforges, “Adversarial example detection for dnn models: A review and experimental comparison,” Artificial Intelligence Review, pp. 1–60, 2022.\n[11] A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, “Adversarial attacks and defences: A survey,” arXiv preprint arXiv:1810.00069, 2018.\n[12] K. Han, B. Xia, and Y. Li, “2: adversarial domain adaptation to defense with adversarial perturbation removal,” Pattern Recognition, vol. 122, p. 108303, 2022.\n[13] S. Abramsky and C. Hankin, “An introduction to abstract interpretation,” in Abstract Interpretation of declarative languages, vol. 1. Ellis Horwood London, 1987, pp.63–102.\n[14] K. Ghorbal, E. Goubault, and S. Putot, “The zonotope abstract domain taylor1+,” in International conference on computer aided verification. Springer, 2009, pp.627–633.\n[15] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.\n[16] S. Seo, S. Arik, J. Yoon, X. Zhang, K. Sohn, and T. Pfister, “Controlling neural networks with rule representations,” Advances in Neural Information Processing Systems, vol. 34, pp. 11 196–11 207, 2021.\n[17] B. Zhang, T. Cai, Z. Lu, D. He, and L. Wang, “Towards certifying l-infinity robustness using neural networks with l-inf-dist neurons,” in International Conference on Machine Learning. PMLR, 2021, pp. 12 368–12 379.\n[18] K. Pei, Y. Cao, J. Yang, and S. Jana, “Deepxplore: Automated whitebox testing of deep learning systems,” in proceedings of the 26th Symposium on Operating Systems\nPrinciples, 2017, pp. 1–18.\n[19] T. Asano, S. Bitou, M. Motoki, and N. Usui, “In-place algorithm for image rotation,” in International Symposium on Algorithms and Computation. Springer, 2007, pp.704–715.\n[20] A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, “A survey on adversarial attacks and defences,” CAAI Transactions on Intelligence Technology, vol. 6, no. 1, pp. 25–45, 2021.\n[21] P. Cousot and R. Cousot, “Abstract interpretation frameworks,” Journal of logic and computation, vol. 2, no. 4, pp. 511–547, 1992.\n[22] Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 9185–9193.\n[23] S. Park and J. So, “On the effectiveness of adversarial training in defending against adversarial example attacks for image classification,” Applied Sciences, vol. 10, no. 22, p. 8079, 2020.\n[24] K. Ren, T. Zheng, Z. Qin, and X. Liu, “Adversarial attacks and defenses in deep learning,” Engineering, vol. 6, no. 3, pp. 346–360, 2020.\n[25] C. Si, Z. Zhang, F. Qi, Z. Liu, Y. Wang, Q. Liu, and M. Sun, “Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021, pp. 1569–1576.\n[26] V. Fischer, M. C. Kumar, J. H. Metzen, and T. Brox, “Adversarial examples for semantic image segmentation,” arXiv preprint arXiv:1703.01101, 2017.\n[27] H. Kwon and S. Lee, “Textual adversarial training of machine learning model for resistance to adversarial examples,” Security and Communication Networks, vol. 2022, 2022.\n[28] H. Xu, Y. Ma, H.-C. Liu, D. Deb, H. Liu, J.-L. Tang, and A. K. Jain, “Adversarial attacks and defenses in images, graphs and text: A review,” International Journal of Automation and Computing, vol. 17, no. 2, pp. 151–178, 2020.\n[29] W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” arXiv preprint arXiv:1704.01155, 2017.\n[30] T. Bai, J. Luo, J. Zhao, B. Wen, and Q. Wang, “Recent advances in adversarial training for adversarial robustness,” arXiv preprint arXiv:2102.01356, 2021.\n[31] N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in 2016 IEEE symposium on security and privacy (SP). IEEE, 2016, pp. 582–597.\n[32] A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083, 2017.\n[33] F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” arXiv preprint arXiv:1705.07204, 2017.\n[34] P. Cousot and R. Cousot, “Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints,” in Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages, 1977, pp. 238–252.\n[35] J. Bertrane, P. Cousot, R. Cousot, J. Feret, L. Mauborgne, A. Miné, X. Rival, et al., “Static analysis and verification of aerospace software by abstract interpretation,” Foundations and Trends® in Programming Languages, vol. 2, no. 2-3, pp. 71–190, 2015.\n[36] X. Rival and K. Yi, Introduction to static analysis: an abstract interpretation perspective. Mit Press, 2020.\n[37] P. Cousot and R. Cousot, “Basic concepts of abstract interpretation,” in Building the Information Society. Springer, 2004, pp. 359–366.\n[38] L. Pulina and A. Tacchella, “An abstraction-refinement approach to verification of artificial neural networks,” in International Conference on Computer Aided Verification. Springer, 2010, pp. 243–257.\n[39] T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev, “Ai2: Safety and robustness certification of neural networks with abstract interpretation,” in 2018 IEEE symposium on security and privacy (SP). IEEE, 2018, pp. 3–18.\n[40] G. Singh, T. Gehr, M. Mirman, M. Püschel, and M. Vechev, “Fast and effective robustness certification,” Advances in neural information processing systems, vol. 31, 2018.\n[41] M. Mirman, T. Gehr, and M. Vechev, “Differentiable abstract interpretation for provably robust neural networks,” in International Conference on Machine Learning. PMLR, 2018, pp. 3578–3586.\n[42] G. Singh, T. Gehr, M. Püschel, and M. Vechev, “An abstract domain for certifying neural networks,” Proceedings of the ACM on Programming Languages, vol. 3, no. POPL, pp. 1–30, 2019.\n[43] S. Kabir, R. U. Islam, M. S. Hossain, and K. Andersson, “An integrated approach of belief rule base and deep learning to predict air pollution,” Sensors, vol. 20, no. 7, p. 1956, 2020.\n[44] L. Chong, M. M. Abbas, A. M. Flintsch, and B. Higgs, “A rule-based neural network approach to model driver naturalistic behavior in traffic,” Transportation Research Part C: Emerging Technologies, vol. 32, pp. 207–223, 2013.\n[45] Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing, “Harnessing deep neural networks with logic rules,” arXiv preprint arXiv:1603.06318, 2016.\n[46] I. Harmon, S. Marconi, B. Weinstein, Y. Bai, D. Z. Wang, E. P. White, and S. Bohlman, “Improving rare tree species classification using domain knowledge,” 2022.\n[47] M. Kukar, I. Kononenko, et al., “Cost-sensitive learning with neural networks.” in ECAI, vol. 15, no. 27. Citeseer, 1998, pp. 88–94.\n[48] M. R. Hassan, B. Nath, and M. Kirley, “A fusion model of hmm, ann and ga for stock market forecasting,” Expert systems with Applications, vol. 33, no. 1, pp. 171–180, 2007.\n[49] E. Haber and M. Holtzman Gazit, “Model fusion and joint inversion,” Surveys in Geophysics, vol. 34, pp. 675–695, 2013.\n[50] P. Deepan and L. Sudha, “Fusion of deep learning models for improving classification accuracy of remote sensing images,” Journal of Mechanics of Continua and Mathematical Sciences, vol. 14, no. 1, pp. 189–201, 2019.\n[51] L. Breiman, “Bagging predictors,” Machine learning, vol. 24, pp. 123–140, 1996.\n[52] D. H. Wolpert, “Stacked generalization,” Neural networks, vol. 5, no. 2, pp. 241–259, 1992.\n[53] Y. Cao, Y. Lin, S. Ning, H. Pi, J. Zhang, and J. Hu, “Gan-based fusion adversarial training,” in Knowledge Science, Engineering and Management: 15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part III.\nSpringer, 2022, pp. 51–64.\n[54] I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572, 2014.\n[55] M. Andriushchenko, F. Croce, N. Flammarion, and M. Hein, “Square attack: a query-efficient black-box adversarial attack via random search,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII. Springer, 2020, pp. 484-501.\n[56] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.\n[57] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248-255.; G0110356042; https://nccur.lib.nccu.edu.tw//handle/140.119/146895; https://nccur.lib.nccu.edu.tw/bitstream/140.119/146895/1/604201.pdf
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4Dissertation/ Thesis
المؤلفون: 曾昱豪
المساهمون: 資訊工程學系
مصطلحات موضوعية: 人工智慧、變分推斷基於對抗訓練用於端到端的語音合成系統、語音合成系統、隱藏式馬爾可夫模型、深度學習, Artificial Intelligence、VITS、TTS、HMM、deep learning
وصف الملف: 99 bytes; text/html
Relation: http://asiair.asia.edu.tw/ir/handle/310904400/115658; http://asiair.asia.edu.tw/ir/bitstream/310904400/115658/1/index.html
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5
المؤلفون: 张鹏
المساهمون: 徐波
مصطلحات موضوعية: 鸡尾酒会问题, 语音分离, 视觉辅助, 在线流式处理, 生成对抗训练, 计算机科学技术::人工智能, 工学::控制科学与工程
Relation: http://ir.ia.ac.cn/handle/173211/44895
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المؤلفون: 郎佳奇
المساهمون: 曾大军
مصطلحات موضوعية: 隐私保护, 对抗训练, 多任务学习, Transformer神经网络
Relation: http://ir.ia.ac.cn/handle/173211/39076
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8Report
Relation: 体育科学; http://ir.psych.ac.cn/handle/311026/3306
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9
المؤلفون: 李劲鹏
المساهمون: 何晖光
مصطلحات موضوعية: 脑-机接口，情绪识别，深度学习，迁移学习，领域自适应，对抗训练
Relation: http://ir.ia.ac.cn/handle/173211/23899
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10
المؤلفون: 关赫
المساهمون: 谭铁牛, 张兆翔
Relation: 关赫. 基于多视图深度网络模型的视觉场景解析[D]. 北京. 中国科学院研究生院. 2018.; http://ir.ia.ac.cn/handle/173211/21598