博碩士論文 108522034 詳細資訊




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姓名 李昕(Shin Lee)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 融合生成對抗網路及領域知識的分層式影像擴增
(Layer Based Data Augmentation With Generative Adversarial Network Combined Domain Knowledge)
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摘要(中) 根據場域實際開發自動光學瑕疵檢測系統的經驗,瑕疵資料收集困難且標記耗時,經常成為系統開發上的時間瓶頸。為了盡早獲得足夠的資料開始訓練模型,資料擴增的手法是不可或缺的。然而,目前常見的擴增方法:基礎擴增方法(平移、旋轉等)以及大部分使用深度學習的擴增方法並未考慮這些擴增出來的影像是否真實存在,直接使用這些方法可能會產生不符合場域知識的資料。因此,我們希望能開發出一個生產高品質影像、且能使用場域知識進行控制的影像擴增系統。
我們準備了像素標記的生成指引,以此將場域知識加入系統中。由於少量訓練資料的限制,直接使用生成指引影像、套用經典GAN模型Pix2Pix的結果並不理想。在廣泛閱讀文獻後我們獲得啟發,提出分層式影像生成系統(GLC),將使用的電子零件影像分為三層三個元件,融合Rule-based與GAN,從元件庫中提取各層元件、按照領域知識Rule-based影像組合器組合,最後以GAN-based的深度學習修改器進行各層元件邊界處修正。
實驗結果顯示,與Baseline模型Pix2Pix相比,GLC在訓練深度學習模型時可減少79%的訓練資料,且生成出來的影像品質較Pix2Pix更為真實,使用了GLC擴增的分類器與沒擴增時相比,Error rate相對改善率更可達97%。
摘要(英) From the actual factory experience of Automatic Optical Inspection System (AOI System) development, we know that defect data is hard to collect and need a lot of time to label, often makes project behind schedule. If we want to obtain enough data for training in the early stage of project, data augmentation technique is necessary. However, common data augmentation method like standard augmentation (flip, shift) and most of deep learning generative architecture do not consider that if the product data really exist in real world. Directly implement these method may produce data which do not meet domain knowledge. Therefore, we intend to develop a data augmentation system that can generate high quality images and products can be controlled with domain knowledge.
Pixel-wise generative guide are prepared to provide domain knowledge for generation process. Due to limited data, the implementation result of famous GAN model Pix2Pix (use generative guide image as input condition) was unfavorable. After paper researching, we get inspiration and propose Generative Layer Combiner (GLC). A generative system which combine rule-based method and GAN. GLC will separate each image in the dataset into three layers, three components. Three components from component libraries will be combined by rule-based image combiner which follows domain knowledge. After rule-based combination, boundary image of each layer will be refined by GAN-based deep learning modifier.
From the experiment results, we can see that compare to baseline model Pix2Pix, GLC can produce higher quality images and reduce 79% real data requirement when training deep learning model. Moreover, compare to no augmentation CNN classifier, CNN classifier training with GLC augmentation’s performance improve by reduce error rate with relative IMP of 97%.
關鍵字(中) ★ 自動光學瑕疵檢測
★ 電腦視覺
★ 領域知識
★ 影像擴增
★ 生成對抗網路
關鍵字(英) ★ Automatic Optical Inspection System
★ Computer Vision
★ Domain Knowledge
★ Data Augmentation
★ GAN
論文目次 摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 研究貢獻 2
1-4 論文架構 3
第二章 相關研究 4
2-1 深度學習分類器 4
2-2 生成對抗網路 5
2-3 領域知識與相關生成網路 6
第三章 研究方法 10
3-1 使用資料集 11
3-2 影像前處理 12
3-3 影像組合器 15
3-4 深度學習修改器 17
3-4-1 鑑別器 17
3-4-2 生成器 18
3-4-3 Objective Function 21
3-4-4 訓練演算法 23
3-5 系統分析 24
第四章 實驗與討論 27
4-1 實驗一:生成模型訓練與專家判識 27
4-1-1 實驗動機與目的 27
4-1-2 實驗方法 27
4-1-3 實驗結果 30
4-2 實驗二:CNN分類器結果比較 32
4-2-1 實驗動機與目的 32
4-2-2 實驗方法 32
4-2-3 實驗結果 34
第五章 結論與未來展望 38
5-1 論文總結 38
5-2 未來展望 38
參考文獻 40
參考文獻 [1] Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan, “GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification,” Neurocomputing
Volume 321, 2018, Pages 321-331.
[2] Shuanlong Niu , Bin Li, Xinggang Wang , Hui Lin, “Defect Image Sample Generation With GAN for Improving Defect Recognition,” IEEE Transactions on Automation Science and Engineering Volume: 17, 2020, Pages 1611 - 1622.
[3] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” 2017 Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5967 - 5976.
[4] Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh, “LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation,” 2017 International Conference on Learning Representations (ICLR), 2017.
[5] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012.
[6] Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” International Conference on Learning Representations (ICLR), 2015.
[7] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[8] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio,“Generative Adversarial Networks,” Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014), 2014, pp. 2672–2680.
[9] Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley,“Least Squares Generative Adversarial Networks,”2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2813 - 2821.
[10] Mehdi Mirza, Simon Osindero, “Conditional Generative Adversarial Nets,” arXiv preprint arXiv:1411.1784, 2014.
[11] Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros, “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks,” 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2242 - 2251.
[12] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," Proceedings of the IEEE international conference on computer vision, 2017, pp. 618-626.
指導教授 梁德容 張欽圳 審核日期 2021-7-13
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