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In Proceedings of 25th International Conference on Very Large Data Bases, volume 99, pages 518–529, 1999. [Gong et al., 2013] Yunchao Gong, Svetlana Lazebnik, Albert Gor￾do, and Florent Perronnin. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelli￾gence, 35(12):2916–2929, 2013. [Heo et al., 2012] Jae-Pil Heo, Youngwoon Lee, Junfeng He, Shih￾Fu Chang, and Sung-Eui Yoon. Spherical hashing. In Proceed￾ings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2957–2964, 2012. [Indyk and Motwani, 1998] Piotr Indyk and Rajeev Motwani. Ap￾proximate nearest neighbors: towards removing the curse of di￾mensionality. In Proceedings of the thirtieth annual ACM sym￾posium on Theory of computing, pages 604–613, 1998. [Kang et al., 2016] Wang-Cheng Kang, Wu-Jun Li, and Zhi-Hua Zhou. Column sampling based discrete supervised hashing. In Proceedings of the Thirtieth AAAI Conference on Artificial Intel￾ligence, pages 1230–1236, 2016. [Kong and Li, 2012] Weihao Kong and Wu-Jun Li. Isotropic hash￾ing. In Advances in Neural Information Processing Systems, pages 1646–1654, 2012. [Krizhevsky and Hinton, 2009] Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. 2009. [Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Ge￾offrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pages 1097–1105. 2012. [Lai et al., 2015] Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. Simultaneous feature learning and hash coding with deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3270–3278, 2015. [Lee and Seung, 1999] Daniel D. Lee and H. Sebastian Seung. Learning the parts of objects by non-negative matrix factoriza￾tion. Nature, 401(6755):788–791, 1999. [Li et al., 2016] Wu-Jun Li, Sheng Wang, and Wang-Cheng Kang. Feature learning based deep supervised hashing with pairwise la￾bels. In Proceedings of the Twenty-Fifth International Joint Con￾ference on Artificial Intelligence, pages 1711–1717, 2016. [Lin et al., 2014] Guosheng Lin, Chunhua Shen, Qinfeng Shi, An￾ton van den Hengel, and David Suter. Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni￾tion, pages 1963–1970, 2014. [Liu et al., 2010] Wei Liu, Junfeng He, and Shih-Fu Chang. Large graph construction for scalable semi-supervised learning. In Pro￾ceedings of the 27th International Conference on Machine Learn￾ing, pages 679–686, 2010. [Liu et al., 2011] Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Hashing with graphs. In Proceedings of the 28th Inter￾national Conference on Machine Learning, pages 1–8, 2011. [Liu et al., 2016] Haomiao Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. Deep supervised hashing for fast image retrieval. In Proceedings of IEEE Conference on Computer Vision and Pat￾tern Recognition, pages 2064–2072, 2016. [Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pages 3111–3119, 2013. [Oliva and Torralba, 2001] Aude Oliva and Antonio Torralba. Mod￾eling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145–175, 2001. [Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: online learning of social representations. In Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining, pages 701–710, 2014. [Shen et al., 2015] Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. Supervised discrete hashing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 37–45, 2015. [Vedaldi and Lenc, 2014] Andrea Vedaldi and Karel Lenc. Matcon￾vnet - convolutional neural networks for MATLAB. CoRR, ab￾s/1412.4564, 2014. [Wang et al., 2012] Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Semi-supervised hashing for large-scale search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(12):2393–2406, 2012. [Weiss et al., 2009] Yair Weiss, Antonio Torralba, and Rob Fergus. Spectral hashing. In Advances in Neural Information Processing Systems, pages 1753–1760, 2009. [Weston et al., 2012] Jason Weston, Fred´ eric Ratle, Hossein ´ Mobahi, and Ronan Collobert. Deep learning via semi￾supervised embedding. In Neural Networks: Tricks of the Trade, pages 639–655. Springer, 2012. [Xia et al., 2014] Rongkai Xia, Yan Pan, Hanjiang Lai, Cong Liu, and Shuicheng Yan. Supervised hashing for image retrieval via image representation learning. In Proceedings of AAAI Confer￾ence on Artificial Intelligence, pages 2156–2162, 2014. [Yang et al., 2016] Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In Proceedings of the 33nd International Confer￾ence on Machine Learning, pages 40–48, 2016. [Zhang et al., 2014] Peichao Zhang, Wei Zhang, Wu-Jun Li, and Minyi Guo. Supervised hashing with latent factor models. In Pro￾ceedings of the 37th International ACM Conference on Research and Development in Information Retrieval, pages 173–182, 2014. [Zhang et al., 2016] Jian Zhang, Yuxin Peng, and Junchao Zhang. Ssdh: semi-supervised deep hashing for large scale image re￾trieval. arXiv preprint arXiv:1607.08477, 2016. [Zhou et al., 2004] Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Scholkopf. ¨ Learning with local and global consistency. In Advances in Neural Information Processing Systems, pages 321–328, 2004. [Zhu et al., 2016] Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. Deep hashing network for efficient similarity retrieval. In Proceedings of the Thirtieth AAAI Conference on Artificial In￾telligence, pages 2415–2421, 2016. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) 3244
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