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1Dissertation/ Thesis
المؤلفون: 李宗叡, Lee, Tsung-Jui
المساهمون: 資訊管理系, 施再繁, 呂慈純, Shih, Tzay-Farn, Lu, Tzu-Chuen
مصطلحات موضوعية: 卷積神經網路, 影像辨識, 深度學習, 資料增強, Convolutional Neural Networks, Image Recognition, Deep Learning, Data Augmentation
وصف الملف: 1408537 bytes; application/pdf
Relation: 112CYUT0396021; http://ir.lib.cyut.edu.tw:8080/handle/310901800/43777
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2Dissertation/ Thesis
المؤلفون: 林宜佑, Lin, Yi-Yu
المساهمون: 蔡炎龍, Tsai, Yen-Lung
مصطلحات موضوعية: 深度學習, 資料增強, TSGM, 時間序列, 卷積神經網路, 長短期記憶神經網路, 孿生神經網路, 對比學習, 股票走勢預測, Deep Learning, Data Augmentation, Time Series, CNN, LSTM, Siamese Networks, Contrastive Learning, Stock Trend Prediction
وصف الملف: 2373408 bytes; application/pdf
Relation: [1] Yihao Ang, Qiang Huang, Yifan Bao, Anthony KH Tung, and Zhiyong Huang. Tsgbench:Time series generation benchmark. arXiv preprint arXiv:2309.03755, 2023. [2] Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. Signature verification using a” siamese” time delay neural network. Advances in neural information processing systems, 6, 1993. [3] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597–1607. PMLR, 2020. [4] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15750–15758, 2021. [5] Jeffrey L Elman. Finding structure in time. Cognitive science, 14(2):179–211, 1990. [6] Thomas Epelbaum. Deep learning: Technical introduction. arXiv preprint arXiv:1709.01412, 2017. [7] Kunihiko Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 36(4):193–202, 1980. [8] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks, 2014. [9] A Graves, M Liwicki, S Fernandez, R Bertolami, H Bunke, and J Schmidhuber. A novel connectionist system for improved unconstrained handwriting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 2009. [10] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271–21284, 2020. [11] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729–9738, 2020. [12] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. [13] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. [14] Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. Data augmentation for time series classification using convolutional neural networks. In ECML/PKDD workshop on advanced analytics and learning on temporal data, 2016. [15] Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989. [16] Alexander Nikitin, Letizia Iannucci, and Samuel Kaski. Tsgm: A flexible framework for generative modeling of synthetic time series. arXiv preprint arXiv:2305.11567, 2023. [17] Khandakar M Rashid and Joseph Louis. Window-warping: A time series data augmentation of imu data for construction equipment activity identification. In ISARC. Proceedings of the international symposium on automation and robotics in construction, volume 36, pages 651–657. IAARC Publications, 2019. [18] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386, 1958. [19] David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986. [20] David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354–359, 2017. [21] HR Xu, Bo Xu, and KW Xu. Analysis on application of machine learning in stock forecasting. Computer Engineering and Applications, 56(12):19–24, 2020. [22] Jan Yen. Time series representation learning for stock market prediction. 2021.; G0111751004; https://nccur.lib.nccu.edu.tw//handle/140.119/152819; https://nccur.lib.nccu.edu.tw/bitstream/140.119/152819/1/100401.pdf
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3Dissertation/ Thesis
المؤلفون: 林裕哲, LIN, YU-JHE
المساهمون: 資訊與通訊系, 朱鴻棋, CHU, HUNG-CHI
مصطلحات موضوعية: 生成對抗網路, 生成器, 鑑別器, 資料增強, Generative Adversarial Networks, Generator, Discriminator, Data Augmentation
وصف الملف: 1914249 bytes; application/pdf
Relation: 112CYUT0652006; http://ir.lib.cyut.edu.tw:8080/handle/310901800/43047; http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/43047/1/112CYUT0652006-003.pdf
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4
المؤلفون: 林承憲, Lin, Cheng-Shian
المساهمون: 林政宏, Lin, Cheng-Hung
مصطلحات موضوعية: 深度神經網路, 資料增強, 分布外偵測, 異常偵測, 離群偵測, deep neural network, data augmentation, out-of-distribution detection, anomaly detection, outlier detection
وصف الملف: application/pdf
Relation: 60775054H-38383; https://etds.lib.ntnu.edu.tw/thesis/detail/a775f2d5bdf2be29b9279ae9b928e822/; http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/116951
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5Dissertation/ Thesis
المؤلفون: 彭建凱, Peng, Chien-Kai
المساهمون: 廖文宏, Liao, Wen-Hung
مصطلحات موضوعية: 深度學習, 圖像分類, 遷移學習, 資料增強, 模型可解釋化, 導線熔痕分析, Deep learning, Image classification, Transfer learning, Data Augmentation, Model interpretability, Metallographic Analysis
وصف الملف: 8394257 bytes; application/pdf
Relation: [1] 中華民國內政部消防署全球資訊網 火災統計 https://www.nfa.gov.tw/cht/index.php?code=list&ids=220\n[2] 中華民國內政部消防署全球資訊網 修正「火災調查鑑定標準作業程序」、「火災原因調查鑑定書製作規定」、「火災原因調查鑑定書分級列管實施規定」之名稱及規定 https://www.nfa.gov.tw/cht/index.php?code=list&flag=detail&ids=23&article_id=343\n[3] Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 27.\n[4] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.\n[5] Elkan, C. (2001, August). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, No. 1, pp. 973-978). Lawrence Erlbaum Associates Ltd.\n[6] Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018, October). A survey on deep transfer learning. In International conference on artificial neural networks (pp. 270-279). Springer, Cham.\n[7] Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI magazine, 38(3), 50-57.\n[8] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921-2929).\n[9] Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400.\n[10] Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618-626).\n[11] Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806.\n[12] Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018, March). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 839-847). IEEE.\n[13] ImageNet http://www.image-net.org/\n[14] Coco DataSet https://cocodataset.org/\n[15] Open Image DataSet https://storage.googleapis.com/openimages/web/index.html\n[16] Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017, February). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).; G0107971001; https://nccur.lib.nccu.edu.tw//handle/140.119/134202; https://nccur.lib.nccu.edu.tw/bitstream/140.119/134202/1/100101.pdf
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6Dissertation/ Thesis
المؤلفون: 王竣煌, Wang, Chun-Huang
المساهمون: 資訊工程學系, 吳宗憲, Wu, Chung-Hsien, 王竣煌, Wang, Chun-Huang
مصطلحات موضوعية: 自動語音辨識, 低資源語言, 語碼轉換, 資料增強, automatic speech recognition, under-resourced language, code-switching, data augmentation, lang, litt
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7Dissertation/ Thesis
المؤلفون: 黃健傑
مصطلحات موضوعية: 缺失值處理, 資料增強, 動態訓練資料, 雨量預測, Missing data, Data augmentation, Dynamic training, Rainfall forecast
وصف الملف: 169 bytes; text/html
Relation: http://ir.lib.ntust.edu.tw/handle/987654321/77830; http://ir.lib.ntust.edu.tw/bitstream/987654321/77830/1/index.html
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8Dissertation/ Thesis
المؤلفون: 吳信賢, Wu, Shin-Shian
المساهمون: 廖文宏, Liao, Wen-Hung
مصطلحات موضوعية: 深度學習, 物體偵測, 遷移學習, 資料增強, 衛照圖資分析, Deep learning, Object detection, Transfer learning, Data augmentation, Satellite image
وصف الملف: 3287995 bytes; application/pdf
Relation: G0104971022; http://nccur.lib.nccu.edu.tw//handle/140.119/119236; http://nccur.lib.nccu.edu.tw/bitstream/140.119/119236/1/102201.pdf
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9Dissertation/ Thesis
المؤلفون: 吳信賢, Wu, Shin-Shian
المساهمون: 廖文宏, Liao, Wen-Hung
مصطلحات موضوعية: 深度學習, 物體偵測, 遷移學習, 資料增強, 衛照圖資分析, Deep learning, Object detection, Transfer learning, Data augmentation, Satellite image
وصف الملف: 3287995 bytes; application/pdf
Relation: [1] ImageNet Large Scale Visual Recognition Challenge form http://www.image-net.org/challenges/LSVRC/\n[2] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.\n[3] The PASCAL Visual Object Classes form ttp://host.robots.ox.ac.uk/pascal/VOC/\n[4] ImageNet data set from http://image-net.org/\n[5] Cocodataset form http://cocodataset.org/#home\n[6] Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015.\n[7] He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.\n[8] Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.\n[9] Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.\n[10] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." arXiv preprint arXiv:1708.02002 (2017).\n[11] Girshick, Ross. "Fast r-cnn." arXiv preprint arXiv:1504.08083 (2015).\n[12] Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." arXiv preprint (2017).\n[13] YOLO v2 from https://www.youtube.com/watch?time_continue=3&v=VOC3huqHrss\n[14] Ma, Zhong, et al. "Satellite imagery classification based on deep convolution network." Int. J. Comput. Autom. Control Inf. Eng 10 (2016): 1055-1059.\n[15] Albert, Adrian, Jasleen Kaur, and Marta C. Gonzalez. "Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.\n[16] Van Etten, Adam. "You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery." arXiv preprint arXiv:1805.09512 (2018).\n[17] You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks from https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571\n[18] Google Earth from https://earth.google.com/web/\n[19] arcgis-earth from http://www.esri.com/software/arcgis-earth\n[20] ESRI from https://www.esri.com/en-us/home\n[21] labelImg from https://github.com/tzutalin/labelImg\n[22] Tensorflow from https://www.tensorflow.org/\n[23] Keras from https://github.com/keras-team/keras\n[24] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2010): 1345-1359.\n[25] Huang, Jonathan, et al. "Speed/accuracy trade-offs for modern convolutional object detectors." IEEE CVPR. 2017.\n[26] Chen, Liang-Chieh, et al. "Rethinking atrous convolution for semantic image segmentation." arXiv preprint arXiv:1706.05587 (2017).\n[27] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.; G0104971022; https://nccur.lib.nccu.edu.tw//handle/140.119/119236; https://nccur.lib.nccu.edu.tw/bitstream/140.119/119236/1/102201.pdf