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博一下學期： 1.week1,2018.2.26 2006-Extreme learning machine: theory and applications 期刊來源：Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1-3): 489-501. 2.week2,2018.3.5 2017-3d-prnn: Generating shape primitives with recurrent neural networks University of Illinois at Urbana-Champaign, Adobe Research(美國伊利諾伊大學厄巴納 - 香檳分校，Adobe研究院) 期刊來源：Zou C, Yumer E, Yang J, et al. 3d-prnn: Generating shape primitives with recurrent neural networks[C]//The IEEE International Conference on Computer Vision (ICCV). 2017. 3.week3,2018.3.12;week7,2018.4.9;week8,2018.4.16;week9,2018.4.23 2017-3D object reconstruction from a single depth view with adversarial learning University of Oxford,University of Warwick,Heriot-Watt University(英國牛津大學，華威大學，赫瑞瓦特大學) 期刊來源：Yang B, Wen H, Wang S, et al. 3D object reconstruction from a single depth view with adversarial learning[J]. ICCV, 2017. 2018-3D Object Dense Reconstruction from a Single Depth View 期刊來源:Yang B, Rosa S, Markham A, et al. 3D Object Dense Reconstruction from a Single Depth View[J]. arXiv preprint arXiv:1802.00411, 2018. Improved training of wasserstein gans Montreal Institute for Learning Algorithms,Courant Institute of Mathematical Sciences,CIFAR Fellow(美國科技巨頭蒙特利爾學習算法研究所，庫特數學科學研究所，CIFAR研究員) Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of wasserstein gans[C]//Advances in Neural Information Processing Systems. 2017: 5769-5779. Generative adversarial nets 期刊來源：Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680. 4.week4,2018.3.19 2017-Hierarchical surface prediction for 3d object reconstruction University of California, Berkeley(美國加州大學伯克利分校) 期刊來源：H?ne C, Tulsiani S, Malik J. Hierarchical surface prediction for 3d object reconstruction[J]. arXiv preprint arXiv:1704.00710, 2017. 2017-Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs University of California, Berkeley(美國加州大學伯克利分校) 期刊來源：Tatarchenko M, Dosovitskiy A, Brox T. Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs[J]. CoRR, abs/1703.09438, 2017. 5.week5,2018.3.26 2017-3D shape reconstruction from sketches via multi-view convolutional networks University of Massachusetts - Amherst(美國麻省大學阿默斯特分校) 期刊來源：Lun Z, Gadelha M, Kalogerakis E, et al. 3D shape reconstruction from sketches via multi-view convolutional networks[J]. arXiv preprint arXiv:1707.06375, 2017. 2016-3d shape induction from 2d views of multiple objects University of Massachusetts - Amherst(美國麻省大學阿默斯特分校) 期刊來源：Gadelha M, Maji S, Wang R. 3d shape induction from 2d views of multiple objects[J]. arXiv preprint arXiv:1612.05872, 2016. 2017-Multi-view 3D face reconstruction with deep recurrent neural networks Computational Biomedicine Lab，University of Houston(美國休斯頓大學,計算生物醫學實驗室) 期刊來源：Dou P, Kakadiaris I A. Multi-view 3D face reconstruction with deep recurrent neural networks[C]//Biometrics (IJCB), 2017 IEEE International Joint Conference on. IEEE, 2017: 483-492. 2017-End-to-end 3D face reconstruction with deep neural networks Computational Biomedicine Lab，University of Houston(美國休斯頓大學,計算生物醫學實驗室) 期刊來源：Dou P, Shah S K, Kakadiaris I A. End-to-end 3D face reconstruction with deep neural networks[C]//Proc. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii. 2017, 5. 6.week6,2018.4.2 2017-Weakly supervised generative adversarial networks for 3d reconstruction Stanford University(美國斯坦福大學) 期刊來源：Gwak J Y, Choy C B, Garg A, et al. Weakly supervised generative adversarial networks for 3d reconstruction[J]. arXiv preprint arXiv:1705.10904, 2017. 2016-Unsupervised learning of 3d structure from images NYU Multimedia and Visual Computing Lab(紐約大學,多媒體和視覺計算實驗室) Courant Institute of Mathematical Science(庫蘭特學院,數學科學研究所) NYU Tandon School of Engineering, USA(紐約大學工學院) 期刊來源：Rezende D J, Eslami S M A, Mohamed S, et al. Unsupervised learning of 3d structure from images[C]//Advances In Neural Information Processing Systems. 2016: 4996-5004. 2017-Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning Google DeepMind 期刊來源：Wang L, Fang Y. Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning[J]. arXiv preprint arXiv:1711.09312, 2017. 2017-Began: Boundary equilibrium generative adversarial networks Google 期刊來源：Berthelot D, Schumm T, Metz L. Began: Boundary equilibrium generative adversarial networks[J]. arXiv preprint arXiv:1703.10717, 2017. 7.week9,2018.4.23 2016-Learning a predictable and generative vector representation for objects Robotics Institute, Carnegie Mellon University, MITRE Corporation(卡內基梅隆大學,機器人研究所，MITRE公司) 期刊來源：Girdhar R, Fouhey D F, Rodriguez M, et al. Learning a predictable and generative vector representation for objects[C]//European Conference on Computer Vision. Springer, Cham, 2016: 484-499. 2017-Marrnet: 3d shape reconstruction via 2.5 d sketches MIT CSAIL,ShanghaiTech University,Shanghai Jiao Tong University(麻省理工學院 計算機科學與人工智能實驗室,上海科技大學,上海交通大學) 期刊來源：Wu J, Wang Y, Xue T, et al. Marrnet: 3d shape reconstruction via 2.5 d sketches[C]//Advances In Neural Information Processing Systems. 2017: 540-550. 2016-An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning National University of DefenseTechnology(國防科技大學) 期刊來源：Wang Y, Xie Z, Xu K, et al. An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning[J]. Neurocomputing, 2016, 174: 988-998. 2018-On the convergence of adam and beyond Google New York 期刊來源：Reddi S J, Kale S, Kumar S. On the convergence of adam and beyond[C]//International Conference on Learning Representations. 2018. 8.week13,2018.5.21 2018-Spherical CNNs University of Amsterdam(荷蘭阿姆斯特丹大學) 期刊來源：Cohen T S, Geiger M, Koehler J, et al. Spherical CNNs[J]. ICLR, 2018. 2016-Group equivariant convolutional networks University of Amsterdam(荷蘭阿姆斯特丹大學) 期刊來源：Cohen T, Welling M. Group equivariant convolutional networks[C]//International Conference on Machine Learning. 2016: 2990-2999. 2017-Learning SO(3) Equivariant Representations with Spherical CNNs University of Pennsylvania,Google(美國賓夕法尼亞大學) 期刊來源：Esteves C, Allen-Blanchette C, Makadia A, et al. Learning SO(3) Equivariant Representations with Spherical CNNs[J]. 2017. 2018-HexaConv University of Amsterdam(荷蘭阿姆斯特丹大學) 期刊來源：Hoogeboom E, Peters J W T, Cohen T S, et al. HexaConv[J]. arXiv preprint arXiv:1803.02108, 2018. 9.week15,2018.6.4 2016-View synthesis by appearance flow University of California, Berkeley(美國加州大學伯克利分校) 期刊來源：Zhou T, Tulsiani S, Sun W, et al. View synthesis by appearance flow[C]//European conference on computer vision. Springer, Cham, 2016: 286-301.

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