博碩士論文 105221010 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:25 、訪客IP:44.200.32.31
姓名 江建衛(Jiang Jian Wei)  查詢紙本館藏   畢業系所 數學系
論文名稱 生成對抗網路在影像填補的應用
(Application of Generative Adversarial Networks to Image Inpainting)
相關論文
★ 遲滯型細胞神經網路似駝峰行進波之研究★ 穩態不可壓縮那維爾-史托克問題的最小平方有限元素法之片狀線性數值解
★ Global Exponential Stability of Modified RTD-based Two-Neuron Networks with Discrete Time Delays★ 二維穩態不可壓縮磁流體問題的迭代最小平方有限元素法之數值計算
★ 兩種迭代最小平方有限元素法求解不可壓縮那維爾-史托克方程組之研究★ 非線性耦合動力網路的同步現象分析
★ 邊界層和內部層問題的穩定化有限元素法★ 數種不連續有限元素法求解對流佔優問題之數值研究
★ 某個流固耦合問題的有限元素法數值模擬★ 高階投影法求解那維爾-史托克方程組
★ 非靜態反應-對流-擴散方程的高階緊緻有限差分解法★ 二維非線性淺水波方程的Lax-Wendroff差分數值解
★ Numerical Computation of a Direct-Forcing Immersed Boundary Method for Simulating the Interaction of Fluid with Moving Solid Objects★ On Two Immersed Boundary Methods for Simulating the Dynamics of Fluid-Structure Interaction Problems
★ 非穩態複雜流體的人造壓縮性直接施力沉浸邊界法數值模擬★ 模擬自由落體動力行為的接近不可壓縮直接施力沉浸邊界法
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 本文主要運用 Pathak et al. [13] 和 Iizuka et al. [6] 的基本思想,重新建構一個影像填補的生成對抗網路。在硬體設備運算能力的侷限下,我們建置出一個層數較少的神經網路模型,用來達成某些較為簡單的影像填補任務,例如填補主題較單一而且遺失區域比例較低的情況,而本文的主要目標是進行影像中心遺失小區域時的填補工作。為了實現這個目標,我們採用 Goodfellow et al. [4] 提出的生成對抗網路的想法,運用生成網路與對抗網路的相互競爭以加強填補的效能。更明確地說,我們使用卷積層來建構網路,其中生成網路的部分使用 Iizuka et al. [6] 所提到的擴張卷積 [17]。同時,我們採用了 Ioffe 和 Szegedy [7] 的想法,除了最後一層外,所有網路的每一層後都添加標準化層以增強網路的訓練效果。最後模擬實驗結果顯示,我們的生成對抗網路模型可以相當有效地達成主要的填補任務。
摘要(英) Based on the works of Pathak et al. [13] and Iizuka et al. [6], in this thesis, we introduce a simple generative adversarial network approach for image inpainting.
Considering the limitation of computational capacity, we build a simplified model which is able to reconstruct lost or deteriorated parts of images with single context and small missing region. In order to generate the image content of missing region, we mainly employ the generative adversarial network approach proposed by Goodfellow et al. [4]. More specifically, the proposed neural network consists of convolutional layers, where the dilated convolution is used in the generative network. In addition, except the output layer, each layer is equipped with a normalization layer [7] to enhance the overall efficiency of the network. Numerical experiments are performed to demonstrate the good performance of the simplified generative adversarial network for image inpainting.
關鍵字(中) ★ 神經網路
★ 類神經網路
★ 卷積神經網路
★ 生成對抗網路
★ 影像填補
★ 電腦視覺
★ 深度學習
★ 人工智慧
關鍵字(英) ★ neural network
★ artificial neural network
★ convolutional neural network
★ generative adversarial network
★ image inpainting
★ computer vision
★ deep learning
★ artificial intelligence
論文目次 一 前言 . . . 1
二 多層神經網路 . . . 3
2.1 向前傳遞 . . . 4
2.2 反向傳遞 . . . 7
三 卷積神經網路 . . . 11
3.1 向前傳遞 . . . 12
3.2 反向傳遞 . . . 14
3.3 擴張卷積 . . . 17
四 生成對抗網路 . . . 18
4.1 概念介紹 . . . 18
4.2 訓練流程 . . . 19
4.3 誤差函數 . . . 19
五 影像填補 . . . 20
5.1 網路結構 . . . 20
5.2 誤差函數 . . . 22
5.3 訓練模型 . . . 22
5.4 模型優化 . . . 23
六 模擬實驗 . . . 24
6.1 影像資料的前處理 . . . 25
6.2 實驗結果和討論 . . . 25
七 結論 . . . 29
參考文獻 . . .30
參考文獻 [1] C. Barnes, E. Shechtman, D. B. Goldman, and A. Finkelstein, The generalized patch match correspondence algorithm, European Conference on Computer Vision, (2010), pp. 29-43.
[2] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, 2016.
[3] K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36 (1980), pp. 193-202.
[4] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, Advances in Neural Information Processing Systems, 27 (2014), pp. 2672-2680.
[5] D. H. Hubel and T. N. Wiesel, Receptive fields of single neurones in the cat’s striate cortex, The Journal of Physiology, 148 (1959), pp. 574–591.
[6] S. Iizuka, E. Simo-Serra, and H. Ishikawa, Globally and locally consistent image completion, ACM Transactions on Graphics, 36 (2017), pp. 107:1-107:14.
[7] S. Ioffe and C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, 2015. (arXiv:1502.03167v3)
[8] D. P. Kingma and J. L. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations, 2015. (arXiv:1412.6980v9)
[9] A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25 (2012), pp. 1097-1105.
[10] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Computation, 1 (1989), pp. 541-551.
[11] Z. Liu, P. Luo, X. Wang, and X. Tang, Deep learning face attributes in the wild, International Conference on Computer Vision, (2015). (arXiv:1411.7766v3)
[12] W. S. McCulloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5 (1943), pp. 115-133.
[13] D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, Context encoders: feature learning by inpainting, Conference on Computer Vision and Pattern
Recognition, pp. 2536-2544, 2016.
[14] S. Raschka, Python Machine Learning, Packt Publishing Ltd, 2015.
[15] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, 323 (1986), pp. 533-536.
[16] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. V. D. Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, Mastering the game of go with deep neural networks and tree search, Nature, 529 (2016), pp. 484–489.
[17] F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions, International Conference on Learning Representations, 2016. (arXiv:1511.07122v3)
[18] 齋藤康毅著,吳嘉芳譯, Deep Learning:用Python進行深度學習的基礎理論實作, 歐萊禮出版社,台灣,2017。
指導教授 楊肅煜(Suh-Yuh Yang) 審核日期 2019-1-21
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明