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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王鈺強 | |
| dc.contributor.author | Jia-Wei Yan | en |
| dc.contributor.author | 顏嘉緯 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:07:23Z | - |
| dc.date.available | 2023-12-25 | |
| dc.date.copyright | 2019-12-25 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-12-04 | |
| dc.identifier.citation | [1] D. Berthelot, C. Raffel, A. Roy, and I. Goodfellow. Understanding and improving interpolation in autoencoders via an adversarial regularizer. In Proceedings of the International Conference on Learning Representations (ICLR), 2019.
[2] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), 2016. [3] J. Deng, J. Guo, N. Xue, and S. Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. arXiv preprint arXiv:1801.07698, 2019. [4] J. Donahue, P. Krähenbühl, and T. Darrell. Adversarial feature learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2017. [5] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), pages 2672–2680, 2014. [6] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker. Multi-pie. Image and Vision Computing, 28(5):807–813, 2010. [7] R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006. [8] X. He, Y. Zhou, Z. Zhou, S. Bai, and X. Bai. Triplet-center loss for multi-view 3d object retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [9] I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework. In Proceedings of the International Conference on Learning Representations (ICLR), 2017. [10] X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz. Multimodal unsupervised image-toimage translation. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. [11] D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. [12] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998. [13] H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. K. Singh, and M.-H. Yang. Diverse imageto-image translation via disentangled representations. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. [14] Z. Li, C. Xu, and B. Leng. Angular triplet-center loss for multi-view 3d shape retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2019. [15] M.-Y. Liu, T. Breuel, and J. Kautz. Unsupervised image-to-image translation networks. In Advances in Neural Information Processing Systems (NIPS), 2017. [16] W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song. Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [17] W. Liu, Y. Wen, Z. Yu, and M. Yang. Large-margin softmax loss for convolutional neural networks. In Proceedings of the International Conference on Machine Learning (ICML), 2016. [18] A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015. [19] A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in pytorch. In Advances in Neural Information Processing Systems Workshops (NIPS Workshops), 2017. [20] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [21] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representationsina deep network with a local denoising criterion. Journal of Machine Learning Research, 2010. [22] H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu. Cosface: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [23] Y. Wen1, K. Zhang1, Z. Li1, and Y. Qiao. A discriminative feature learning approachfor deep face recognition. In Proceedings of the European Conference on Computer Vision (ECCV), 2016. [24] Y. Zheng, D. K. Pal, and M. Savvides. Ring loss: Convex feature normalization for face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74777 | - |
| dc.description.abstract | 學習可解釋的特徵表示一直是感興趣的主題之一。 大多數現有的研究不能容易地生成或操縱特徵表示，並通過插值生成具有特定語義的圖像。 在本文中，我們提出了一個角度三元組鄰居函數（ATNL），它能夠導出其分佈與語義資訊匹配的潛在特徵表示。 利用ATNL引導的潛在特徵空間，我們進一步利用球面語義內插來生成語義變化的圖像。 我們對MNIST和CMU Multi-PIE數據集的實驗證實了我們的ATNL和球形語義內插對最近的表示學習模型的有效性和強大性。 | zh_TW |
| dc.description.abstract | Learning interpretable representations has been among the topics of interest. Most existing works cannot easily generate or manipulate latent representations which semantically match the images of interest via interpolation. In this paper, we propose an Angular Triplet-Neighbor Loss (ATNL), which is able to derive latent representations whose distribution would match the semantic information. With the latent space guided by ATNL, we further utilize spherical semantic interpolation for generating semantic warping of images. Our experiments on both MNIST and CMU Multi-PIE datasets confirm the effectiveness and robustness of our ATNL and spherical semantic interpolation over recent representation learning models. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:07:23Z (GMT). No. of bitstreams: 1 ntu-108-R06942033-1.pdf: 7043859 bytes, checksum: fbd1c6ac7a4bccaea2e296aa07bc2cb3 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝iii
Acknowledgements v 摘要vii Abstract ix 1 Introduction 1 2 Related Work 5 3 Semantics-Guided Representation Learning 7 3.1 VAE for Representation Learning . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Angular Triplet-Neighbor Loss (ATNL) . . . . . . . . . . . . . . . . . . 8 3.3 Semantics-Guided Image Generation . . . . . . . . . . . . . . . . . . . . 11 3.4 Learning of Our Framework . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.5.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.5.2 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . 13 3.5.3 Visualization via t-SNE projection . . . . . . . . . . . . . . . . . 13 3.5.4 Image Generation via Linear/Spherical Semantic Interpolation . . 14 3.5.5 Quantitative Evaluation . . . . . . . . . . . . . . . . . . . . . . 17 3.5.6 Assessment of Interpolated Images . . . . . . . . . . . . . . . . 17 3.5.7 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.5.8 Analysis of ATNL . . . . . . . . . . . . . . . . . . . . . . . . . 21 4 Conclusion 23 Bibliography 25 | |
| dc.language.iso | en | |
| dc.subject | 影像生成 | zh_TW |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 特徵學習 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Image generation | en |
| dc.subject | Representation learning | en |
| dc.subject | Deep learning | en |
| dc.subject | Machine learning | en |
| dc.subject | Computer vision | en |
| dc.title | 具有語義引導的特徵學習並應用於視覺化影像生成 | zh_TW |
| dc.title | Semantics-Guided Representation Learningwith Applications to Visual Synthesis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳祝嵩,陳駿丞 | |
| dc.subject.keyword | 影像生成,特徵學習,深度學習,機器學習,電腦視覺, | zh_TW |
| dc.subject.keyword | Image generation,Representation learning,Deep learning,Machine learning,Computer vision, | en |
| dc.relation.page | 27 | |
| dc.identifier.doi | 10.6342/NTU201904354 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-12-04 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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