Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3343031.3350979acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Open Set Deep Learning with A Bayesian Nonparametric Generative Model

Published: 15 October 2019 Publication History

Abstract

Being a widely studied model in machine learning and multimedia community, Deep Neural Network (DNN) has achieved an encouraging success in various applications. However, conventional DNN suffers the difficulty when handling the open set learning problem, in which the true class number is unknown, and the predication label in the testing dataset usually has unseen classes which are not contained in the training set. In this paper, we aim to tackle this problem by unifying deep neural network and Dirichlet process mixture model. Firstly, to learn the deep feature and enable the incorporation of DNN and the Bayesian nonparametric model, we extend deep metric learning to a semi-supervised framework. Secondly, with the learned deep feature, we construct our open set classification method by expanding the Dirichlet process mixture model to a semi-supervised framework. To infer our semi-supervised Bayesian model, the corresponding variational inference algorithm has also been derived. Experiment on synthetic and real world datasets validates our theory analysis and demonstrates the state-of-the-art performance.

References

[1]
Abhijit Bendale and Terrance E. Boult. 2016. Towards Open Set Deep Networks. In CVPR. 1563--1572.
[2]
David M Blei, Michael I Jordan, et almbox. 2006. Variational inference for Dirichlet process mixtures. Bayesian analysis, Vol. 1, 1 (2006), 121--143.
[3]
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018. Deep Clustering for Unsupervised Learning of Visual Features. In The European Conference on Computer Vision (ECCV) .
[4]
Behnam Gholami and Vladimir Pavlovic. 2017. Probabilistic Temporal Subspace Clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3066--3075.
[5]
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality Reduction by Learning an Invariant Mapping. In CVPR, Vol. 2. 1735--1742.
[6]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. computer vision and pattern recognition (2016), 770--778.
[7]
Xiaofei He, Deng Cai, Yuanlong Shao, Hujun Bao, and Jiawei Han. 2011. Laplacian regularized gaussian mixture model for data clustering. IEEE Transactions on Knowledge and Data Engineering, Vol. 23, 9 (2011), 1406--1418.
[8]
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely Connected Convolutional Networks. computer vision and pattern recognition (2017), 2261--2269.
[9]
Yangbangyan Jiang, Zhiyong Yang, Qianqian Xu, Xiaochun Cao, and Qingming Huang. 2018. When to Learn What: Deep Cognitive Subspace Clustering. acm multimedia (2018), 718--726.
[10]
Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. 2017. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering. In IJCAI .
[11]
Elyor Kodirov, Tao Xiang, and Shaogang Gong. 2017. Semantic Autoencoder for Zero-Shot Learning. computer vision and pattern recognition (2017), 4447--4456.
[12]
Marc T Law, Yaoliang Yu, Matthieu Cord, and Eric P Xing. 2016. Closed-form training of mahalanobis distance for supervised clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 3909--3917.
[13]
Xin Li and Fuxin Li. 2017. Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics. international conference on computer vision (2017), 5775--5783.
[14]
Yan Li, Junge Zhang, Jianguo Zhang, and Kaiqi Huang. 2018. Discriminative Learning of Latent Features for Zero-Shot Recognition. computer vision and pattern recognition (2018).
[15]
Fei Tony Liu, Kai Ming Ting, and Zhihua Zhou. 2008. Isolation Forest. (2008).
[16]
Jiwen Lu, Junlin Hu, and Yap-Peng Tan. 2017a. Discriminative Deep Metric Learning for Face and Kinship Verification. IEEE Transactions on Image Processing, Vol. 26, 9 (2017), 4269--4282.
[17]
Jiwen Lu, Junlin Hu, and Jie Zhou. 2017b. Deep Metric Learning for Visual Understanding: An Overview of Recent Advances. IEEE Signal Processing Magazine, Vol. 34, 6 (2017), 76--84.
[18]
Xin Mu, Kai Ming Ting, and Zhihua Zhou. 2017. Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees. IEEE Transactions on Knowledge and Data Engineering, Vol. 29, 8 (2017), 1605--1618.
[19]
Sameer A Nene, Shree K Nayar, Hiroshi Murase, et almbox. 1996. Columbia object image library (coil-20). (1996).
[20]
Tien Vu Nguyen, Dinh Phung, XuanLong Nguyen, Swetha Venkatesh, and Hung Bui. 2014. Bayesian nonparametric multilevel clustering with group-level contexts. In International Conference on Machine Learning. 288--296.
[21]
Konstantina Palla, Zoubin Ghahramani, and David A Knowles. 2012. A nonparametric variable clustering model. In Advances in Neural Information Processing Systems. 2987--2995.
[22]
Anh T Pham, Raviv Raich, Xiaoli Z Fern, and Jesus Perez Arriaga. 2015. Multi-instance multi-label learning in the presence of novel class instances. ICML (2015), 2427--2435.
[23]
Alex Rodriguez and Alessandro Laio. 2014. Clustering by fast search and find of density peaks. Science, Vol. 344, 6191 (2014), 1492--1496.
[24]
Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. FaceNet: A unified embedding for face recognition and clustering. In CVPR . 815--823.
[25]
Edgar Simo-Serra, Carme Torras, and Francesc Moreno-Noguer. 2017. 3D human pose tracking priors using geodesic mixture models. International Journal of Computer Vision, Vol. 122, 2 (2017), 388--408.
[26]
Julian Straub, Oren Freifeld, Guy Rosman, John J. Leonard, and John W. Fisher III. 2017. The Manhattan Frame Model--Manhattan World Inference in the Space of Surface Normals. In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) .
[27]
Roberto Tron and René Vidal. 2007. A benchmark for the comparison of 3-d motion segmentation algorithms. In CVPR. IEEE, 1--8.
[28]
Yong Wang, Yuan Jiang, Yi Wu, and Zhi-Hua Zhou. 2011. Spectral clustering on multiple manifolds. IEEE Transactions on Neural Networks, Vol. 22, 7 (2011), 1149--1161.
[29]
Y. Wang and G Mori. 2009. Human action recognition by semilatent topic models. IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 31, 10 (2009), 1762.
[30]
Yining Wang and Jun Zhu. 2015. DP-space: Bayesian nonparametric subspace clustering with small-variance asymptotics. In ICML-15. 862--870.
[31]
Xin Wei and Zhen Yang. 2012. The infinite Student's t-factor mixture analyzer for robust clustering and classification. Pattern Recognition, Vol. 45, 12 (2012), 4346--4357.
[32]
Tingfan Wu, Chihjen Lin, and Ruby C Weng. 2004. Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research, Vol. 5, 5 (2004), 975--1005.
[33]
Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In International Conference on Machine Learning. 478--487.
[34]
Yang Yang, Yadan Luo, Weilun Chen, Fumin Shen, Jie Shao, and Heng Tao Shen. 2016. Zero-Shot Hashing via Transferring Supervised Knowledge. acm multimedia (2016), 1286--1295.
[35]
Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, and Yuan Jiang. 2017. Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. In IJCAI . 3315--3321.
[36]
Xulun Ye and Jieyu Zhao. 2019. Multi-manifold Clustering: A Graph-constrained Deep Nonparametric Method. Pattern Recognition, Vol. 93 (2019), 215--227.
[37]
Xulun Ye, Jieyu Zhao, and Yu Chen. 2018. A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency. Entropy, Vol. 20, 11 (2018), 830.
[38]
Xulun Ye, Jieyu Zhao, Long Zhang, and Lijun Guo. 2019. A Nonparametric Deep Generative Model for Multimanifold Clustering. IEEE Transactions on Cybernetics, Vol. 49, 7 (2019), 2664--2677.
[39]
Huaiwen Zhang, Quan Fang, Shengsheng Qian, and Changsheng Xu. 2018. Learning Multimodal Taxonomy via Variational Deep Graph Embedding and Clustering. acm multimedia (2018), 681--689.
[40]
Yue Zhu, Kai Ming Ting, and Zhihua Zhou. 2017a. Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. AAAI (2017), 2977--2984.
[41]
Yue Zhu, Kai Ming Ting, and Zhi Hua Zhou. 2017b. Multi-label Learning with Emerging New Labels. In IEEE International Conference on Data Mining . 1--1.

Cited By

View all

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
MM '19: Proceedings of the 27th ACM International Conference on Multimedia
October 2019
2794 pages
ISBN:9781450368896
DOI:10.1145/3343031
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. deep neural networks
  2. dirichlet process
  3. metric learning
  4. open set learning

Qualifiers

  • Research-article

Funding Sources

  • K. C.Wong Magna Fund in Ningbo University
  • National Natural Science Foundation of China
  • National Natural Science Foundation of Zhejiang Province

Conference

MM '19
Sponsor:

Acceptance Rates

MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)13
  • Downloads (Last 6 weeks)4
Reflects downloads up to 25 Nov 2024

Other Metrics

Citations

Cited By

View all

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media