請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79455完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王勝德(Sheng-De Wang) | |
| dc.contributor.author | Wei-Chen Yeh | en |
| dc.contributor.author | 葉韋辰 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:00:56Z | - |
| dc.date.available | 2021-11-05 | |
| dc.date.available | 2022-11-23T09:00:56Z | - |
| dc.date.copyright | 2021-11-05 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-19 | |
| dc.identifier.citation | M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International Conference on Machine Learning. PMLR, 2019, pp. 6105–6114. M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2820–2828. M. Zhu and S. Gupta, “To prune, or not to prune: exploring the efficacy of pruning for model compression,” arXiv preprint arXiv:1710.01878, 2017. Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, “Amc: Automl for model compression and acceleration on mobile devices,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 784–800. K. Wang, Z. Liu, Y. Lin, J. Lin, and S. Han, “Haq: Hardware-aware automated quantization with mixed precision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8612–8620. Y. Liu, C. Shun, J. Wang, and C. Shen, “Structured knowledge distillation for dense prediction,” arXiv preprint arXiv:1903.04197, 2019. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning. PMLR, 2015, pp. 448–456. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inceptionresnet and the impact of residual connections on learning,” arXiv preprint arXiv:1602.07261, 2016. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510–4520. X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6848–6856. N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “Shufflenet v2: Practical guidelines for efficient cnn architecture design,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 116–131. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size,” arXiv preprint arXiv:1602.07360, 2016. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141. H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” arXiv preprint arXiv:1806.09055, 2018. X. Chen, L. Xie, J. Wu, and Q. Tian, “Progressive differentiable architecture search: Bridging the depth gap between search and evaluation,” in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 1294–1303. G. Li, G. Qian, I. C. Delgadillo, M. Muller, A. Thabet, and B. Ghanem, “Sgas: Sequential greedy architecture search,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1620–1630. A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan et al., “Searching for mobilenetv3,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1314–1324. H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” in International Conference on Machine Learning. PMLR, 2018, pp. 4095–4104. H. Cai, L. Zhu, and S. Han, “Proxylessnas: Direct neural architecture search on target task and hardware,” arXiv preprint arXiv:1812.00332, 2018. H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han, “Once-for-all: Train one network and specialize it for efficient deployment,” arXiv preprint arXiv:1908.09791, 2019. M. Li, J. Lin, Y. Ding, Z. Liu, J.-Y. Zhu, and S. Han, “Gan compression: Efficient architectures for interactive conditional gans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 5284–5294. Y. Gao, H. Yang, P. Zhang, C. Zhou, and Y. Hu, “Graphnas: Graph neural architecture search with reinforcement learning,” arXiv preprint arXiv:1904.09981, 2019. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016. W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” arXiv preprint arXiv:1706.02216, 2017. K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” arXiv preprint arXiv:1810.00826, 2018. Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” Acm Transactions On Graphics (tog), vol. 38, no. 5, pp. 1–12, 2019. M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, “Simple and deep graph convolutional networks,” in International Conference on Machine Learning. PMLR, 2020, pp. 1725–1735. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017. G. Li, M. Muller, A. Thabet, and B. Ghanem, “Deepgcns: Can gcns go as deep as cnns?” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 9267–9276. M. Zitnik and J. Leskovec, “Predicting multicellular function through multi-layer tissue networks,” Bioinformatics, vol. 33, no. 14, pp. i190–i198, 2017. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1912–1920. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79455 | - |
| dc.description.abstract | 最近，圖卷積網路的架構越來越深，而圖卷積網路的模型大小和推斷時間也逐漸上升。這篇論文提出 EGASII，其目的在於用神經網路結構搜索來自動得到一個高效率的圖卷積網路架構。這個方法結合PDARTS 和 SGAS 來減少搜尋階段和評估階段的準確度差異。將初始殘差和恆等映射加入候選運算中。這篇論文試著找到怎麼組合和連接這些候選運算，來得到在模型大小和推斷時間上效率較高的架構。結果，使用 EGASII 所得到的架構有比較小的模型大小或比較短的推斷時間。以同樣的模型大小或推斷時間來看，有較高的準確度。在 PPI資料集上，模型大小和推斷時間的效率提高。在 ModelNet 資料集上，模型大小的效率提高。兩個資料集都是節點分類的課題，並且是歸納設定。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:00:56Z (GMT). No. of bitstreams: 1 U0001-1410202101191600.pdf: 1838631 bytes, checksum: 97bb1f66fd4326b6aa38b26358290e81 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 摘要 ii Abstract iii 1 Introduction 1 2 Related Work 3 2.1 Model Compression 3 2.2 Neural Architecture Search 4 2.3 DARTS 6 2.4 PDARTS 7 2.5 SGAS 7 2.6 GCNII 8 2.7 GCN operations 10 3 Approach 12 3.1 Combining PDARTS and SGAS 12 3.2 Integrate Initial residual and Identical mapping to search architecture 15 3.3 Efficient architecture search for GCN 18 4 Experiment 19 4.1 Dataset 19 4.2 PPI dataset 20 4.2.1 Experiment details and derived architecture 20 4.2.2 Result and comparison with other models 20 4.3 ModelNet dataset 21 4.3.1 Experiment details and derived architecture 21 4.3.2 Result and comparison with other models 22 4.4 Discussion 22 4.5 Ablation study 25 5 Conclusion Remarks 26 Bibliography 27 | |
| dc.language.iso | en | |
| dc.title | 使用初始殘差和恆等映射的高效率圖卷積神經網路結構搜索 | zh_TW |
| dc.title | EGASII: Efficient Graph Architecture Search with Initial Residual and Identity Mapping | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李宏毅(Hsin-Tsai Liu),王鈺強(Chih-Yang Tseng),余承叡 | |
| dc.subject.keyword | 深度學習,模型壓縮,神經網路結構搜索,圖卷積網路,過平滑問題, | zh_TW |
| dc.subject.keyword | deep learning,model compression,neural architecture search,graph convolutional network,over-smoothing problem, | en |
| dc.relation.page | 30 | |
| dc.identifier.doi | 10.6342/NTU202103712 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1410202101191600.pdf | 1.8 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。