Abstract
Recent studies attempt to construct complicated and redundant Convolutional Neural Networks (CNNs) to improve image classification performance. In this paper, instead of painstakingly designing a CNN’s architecture, we consider promoting classification performance by revising CNN’s classification results. We therefore propose a novel image classification approach that Learns to Rectify Label (LRL) through Kernel Extreme Learning Machine (KELM). It includes two phases: (1) Pre classification, we put images into a trained CNN to generate corresponding incomplete labels. (2) Label Rectification, the incomplete labels are rectified by the KELM’s high-dimensional map**, so final classification results are acquired. Extensive experiments conducted on public datasets demonstrate the effectiveness of our method. At the meantime, our method has well generalizability that can be integrated with many popular networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarap, A.F.: An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification. ar**v preprint ar**v:1712.03541 (2017)
Bernal, J., et al.: Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Med. 95, 64–81 (2019)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection (2005)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2net: a new multi-scale backbone architecture. ar**v preprint ar**v:1904.01169 (2019)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Identity map**s in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Hou, S., Wang, Z.: Weighted channel dropout for regularization of deep convolutional neural network. In: AAAI (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Huang, G., Sun, Yu., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 646–661. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_39
Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 513–529 (2011)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\)0.5 mb model size. ar**v preprint ar**v:1602.07360 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. ar**v preprint ar**v:1502.03167 (2015)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical Report, Citeseer (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Larsson, G., Maire, M., Shakhnarovich, G.: Fractalnet: ultra-deep neural networks without residuals. ar**v preprint ar**v:1605.07648 (2016)
Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks (2019)
Li, Z., Zhu, X., Wang, L., Guo, P.: Image classification using convolutional neural networks and kernel extreme learning machines. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3009–3013. IEEE (2018)
Lin, M., Chen, Q., Yan, S.: Network in network. ar**v preprint ar**v:1312.4400 (2013)
Lowe, D.G., et al.: Object recognition from local scale-invariant features. In: ICCV, vol. 99, pp. 1150–1157 (1999)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ar**v preprint ar**v:1409.1556 (2014)
Sutskever, I., Vinyals, O., Le, Q.: Sequence to sequence learning with neural networks. In: Advances in NIPS (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Zhu, C., Han, S., Mao, H., Dally, W.J.: Trained ternary quantization. ar**v preprint ar**v:1612.01064 (2016)
Zhu, Y., Zhang, C., Zhou, D., Wang, X., Bai, X., Liu, W.: Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing 214, 758–766 (2016)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61877002), Bei**g Municipal Commission of Education PXM2019_014213_000007, Bei**g Natural Science Foundation, Fengtai Rail Transit Frontier Research Joint Fund 19L00005, and Postgraduate Research Capacity Improvement Program from Bei**g Technology and Business University in 2020.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Cai, Q., Li, F., Li, H., Cao, J., Li, S. (2021). Learn to Rectify Label Through Kernel Extreme Learning Machine. In: Wu, X., Wu, K., Wang, C. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-030-77569-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-77569-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77568-1
Online ISBN: 978-3-030-77569-8
eBook Packages: Computer ScienceComputer Science (R0)