Self-paced learning with privileged information

Abstract Self-paced learning (SPL) is a powerful learning framework, where data from easy ones to more complex ones are gradually involved in the learning process. However, SPL is unable to exploit prior knowledge, so it is prone to overfitting. To alleviate this problem, we propose a framework called self-paced learning with privileged information (SPL+), where privileged information is introduced as prior knowledge to guide the curriculum learned by SPL. Specifically, the learning process using weighted privileged information and the curriculum learning process guided by privileged information are iteratively performed until the final mature curriculum guided by privileged information is learned. As this curriculum learning process can gradually grasp the easy to hard knowledge under the guidance of the robust high level privileged information, a more reliable model can be learned. Moreover, our SPL+ is a generalized framework, which is applicable to various problems. Comprehensive experiments demonstrate that our SPL+ outperforms the conventional SPL based method for three applications including action recognition, scene recognition and handwritten digit recognition.

[1]  Qi Xie,et al.  Self-Paced Co-training , 2017, ICML.

[2]  Daphne Koller,et al.  Self-Paced Learning for Latent Variable Models , 2010, NIPS.

[3]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Nanning Zheng,et al.  Deep self-paced learning for person re-identification , 2017, Pattern Recognit..

[5]  Deva Ramanan,et al.  Self-Paced Learning for Long-Term Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

[7]  Chao Li,et al.  A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Cordelia Schmid,et al.  Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  Deyu Meng,et al.  Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search , 2014, ACM Multimedia.

[10]  Jake K. Aggarwal,et al.  View invariant human action recognition using histograms of 3D joints , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Rama Chellappa,et al.  Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Jan Feyereisl,et al.  Object Localization based on Structural SVM using Privileged Information , 2014, NIPS.

[13]  Dong Xu,et al.  Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Luc Van Gool,et al.  Fast Algorithms for Linear and Kernel SVM+ , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yang Gao,et al.  Self-paced dictionary learning for image classification , 2012, ACM Multimedia.

[16]  Dong Xu,et al.  Exploiting Privileged Information from Web Data for Image Categorization , 2014, ECCV.

[17]  Yiming Ying,et al.  Support Vector Machine Soft Margin Classifiers: Error Analysis , 2004, J. Mach. Learn. Res..

[18]  Dacheng Tao,et al.  Multi-view Self-Paced Learning for Clustering , 2015, IJCAI.

[19]  Christoph H. Lampert,et al.  Learning to Rank Using Privileged Information , 2013, 2013 IEEE International Conference on Computer Vision.

[20]  Antonio Torralba,et al.  Are all training examples equally valuable? , 2013, ArXiv.

[21]  Vladimir Vapnik,et al.  Learning using hidden information: Master-class learning , 2007, NATO ASI Mining Massive Data Sets for Security.

[22]  Zenglin Xu,et al.  Robust Softmax Regression for Multi-class Classification with Self-Paced Learning , 2017, IJCAI.

[23]  Vladimir Vapnik,et al.  A new learning paradigm: Learning using privileged information , 2009, Neural Networks.

[24]  Maoguo Gong,et al.  Self-paced Convolutional Neural Networks , 2017, IJCAI.

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Shiguang Shan,et al.  Self-Paced Learning with Diversity , 2014, NIPS.

[27]  Shiguang Shan,et al.  Self-Paced Curriculum Learning , 2015, AAAI.