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研究生: 孫美雪
Natnuntnita Siriphockpirom
論文名稱: 基於深度學習網路之輪廓一致性圖像轉換
A Contour Consistency Network of Image to Image Translation on Deep Learning
指導教授: 陳永耀
Yung-Yao Chen
口試委員: 林昌鴻
Chang-Hong Lin
林郁修
Yu-Hsiu Lin
花凱龍
Kai-Lung Hua
學位類別: 碩士
Master
系所名稱: 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 33
中文關鍵詞: Image to Image TranslationContour Consistency NetworkInconsistency ProblemAttention Feature Map
外文關鍵詞: Image to Image Translation, Contour Consistency Network, Inconsistency Problem, Attention Feature Map
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  • ABSTRACT ..................................................................................................................... ii ACKNOWLEDGEMENTS............................................................................................. iii CONTENTS .................................................................................................................... iv LIST OF FIGURES ......................................................................................................... vi LIST OF TABLES.......................................................................................................... vii 1. Introduction .............................................................................................................. 1 1.1 Research Background ....................................................................................... 1 1.2 Research Outline............................................................................................... 4 2. Related Work............................................................................................................ 5 2.1 Convolutional Neural Network (CNN) ............................................................ 5 2.2 Generative Adversarial Networks (GAN) ........................................................ 6 2.3 Image to Image Translation ............................................................................ 10 2.4 Unpaired Image-to-Image Translation ........................................................... 12 2.5 Cycle Consistency .......................................................................................... 14 Methodology........................................................................................................... 16 3.1 Overview of the Proposed Framework ........................................................... 16 3.2 Generator and Discriminator .......................................................................... 17 3.3 Contour Consistency Network........................................................................ 18 3.4 Loss Function ................................................................................................. 20 Results .................................................................................................................... 22 4.1 Dataset ............................................................................................................ 22 4.2 System Performance Evaluation..................................................................... 24 Conclusions ............................................................................................................ 30 5.1 Discussion....................................................................................................... 30 REFERENCES ............................................................................................................... 31

    Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2010). Contour detection and hierarchical image segmentation. IEEE transactions on pattern analysis and machine intelligence, 33(5), 898-916.
    Aytar, Y., Castrejon, L., Vondrick, C., Pirsiavash, H., & Torralba, A. (2017). Cross- modal scene networks. IEEE transactions on pattern analysis and machine intelligence, 40(10), 2303-2314.
    Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017).
    Unsupervised pixel-level domain adaptation with generative adversarial networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062.
    Chen, Y.-Y., Li, G.-Y., Jhong, S.-Y., Chen, P.-H., Tsai, C.-C., & Chen, P.-H. (2020). Nighttime pedestrian detection based on thermal imaging and convolutional neural networks. Sensors and Materials, 32(10), 3157-3167.
    Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., . . . Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015). Deep generative image models using a laplacian pyramid of adversarial networks. arXiv preprint arXiv:1506.05751.
    Dosovitskiy, A., & Brox, T. (2016). Generating images with perceptual similarity metrics based on deep networks. Advances in neural information processing systems, 29, 658-666.
    Efros, A. A., & Leung, T. K. (1999). Texture synthesis by non-parametric sampling. Paper presented at the Proceedings of the seventh IEEE international conference on computer vision.
    Godard, C., Mac Aodha, O., & Brostow, G. J. (2017). Unsupervised monocular depth estimation with left-right consistency. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Godard, C., Mac Aodha, O., Firman, M., & Brostow, G. J. (2019). Digging into self- supervised monocular depth estimation. Paper presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
    He, D., Xia, Y., Qin, T., Wang, L., Yu, N., Liu, T.-Y., & Ma, W.-Y. (2016). Dual learning for machine translation. Advances in neural information processing systems, 29, 820-828.
    Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., & Salesin, D. H. (2001). Image analogies. Paper presented at the Proceedings of the 28th annual conference on Computer graphics and interactive techniques.
    Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30.
    Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2016). Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Transactions on Graphics (ToG), 35(4), 1-11.
    Im, D. J., Kim, C. D., Jiang, H., & Memisevic, R. (2016). Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110.
    Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Kalal, Z., Mikolajczyk, K., & Matas, J. (2010). Forward-backward error: Automatic detection of tracking failures. Paper presented at the 2010 20th international conference on pattern recognition.
    Kim, J., Kim, M., Kang, H., & Lee, K. (2019). U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to- image translation. arXiv preprint arXiv:1907.10830.
    Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
    Li, C., & Wand, M. (2016). Combining markov random fields and convolutional neural networks for image synthesis. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Lin, Y.-X., Tan, D. S., Chen, Y.-Y., Huang, C.-C., & Hua, K.-L. (2020). Domain Adaptation With Foreground/Background Cues and Gated Discriminators. IEEE MultiMedia, 27(3), 44-53.
    Liu, M.-Y., Breuel, T., & Kautz, J. (2017). Unsupervised image-to-image translation networks. Paper presented at the Advances in neural information processing systems.
    Liu, M.-Y., & Tuzel, O. (2016). Coupled generative adversarial networks. Advances in neural information processing systems, 29, 469-477.
    Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Mathieu, M., Couprie, C., & LeCun, Y. (2015). Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440.
    Mathieu, M., Zhao, J., Sprechmann, P., Ramesh, A., & LeCun, Y. (2016). Disentangling factors of variation in deep representations using adversarial training. arXiv preprint arXiv:1611.03383.
    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
    Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative adversarial text to image synthesis. Paper presented at the International Conference on Machine Learning.
    Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016). Playing for data: Ground truth from computer games. Paper presented at the European conference on computer vision.
    Rosales, R., Achan, K., & Frey, B. J. (2003). Unsupervised image translation. Paper presented at the iccv.
    Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. Advances in neural information processing systems, 29, 2234-2242.
    Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017).
    Learning from simulated and unsupervised images through adversarial training.
    Paper presented at the Proceedings of the IEEE conference on computer vision
    and pattern recognition.
    Taigman, Y., Polyak, A., & Wolf, L. (2016). Unsupervised cross-domain image
    generation. arXiv preprint arXiv:1611.02200.
    Vondrick, C., Pirsiavash, H., & Torralba, A. (2016). Generating videos with scene
    dynamics. Advances in neural information processing systems, 29, 613-621. Wang, F., Huang, Q., & Guibas, L. J. (2013). Image co-segmentation via consistent
    functional maps. Paper presented at the Proceedings of the IEEE international
    conference on computer vision.
    Wang, L., Li, R., Sun, J., Seah, H. S., Quah, C. K., Zhao, L., & Tandianus, B. (2020).
    Image-similarity-based Convolutional Neural Network for Robot Visual
    Relocalization. Sens. Mater, 32, 1245-1259.
    Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality
    assessment: from error visibility to structural similarity. IEEE transactions on
    image processing, 13(4), 600-612.
    Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016). Learning a
    probabilistic latent space of object shapes via 3d generative-adversarial modeling. Paper presented at the Proceedings of the 30th International Conference on Neural Information Processing Systems.
    Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. Paper presented at the Proceedings of the IEEE international conference on computer vision.
    Yi, Z., Zhang, H., Tan, P., & Gong, M. (2017). Dualgan: Unsupervised dual learning for image-to-image translation. Paper presented at the Proceedings of the IEEE international conference on computer vision.
    Zach, C., Klopschitz, M., & Pollefeys, M. (2010). Disambiguating visual relations using loop constraints. Paper presented at the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
    Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Zhao, J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126.
    Zhou, T., Krahenbuhl, P., Aubry, M., Huang, Q., & Efros, A. A. (2016). Learning dense correspondence via 3d-guided cycle consistency. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
    Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Paper presented at the Proceedings of the IEEE international conference on computer vision.

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