Abstract
Typically, screen defect detection treats different types of defects as a single category and ignores the large variation among them, which may pose large difficulty to the model learning and thus lead to inferior performance. In this paper, we propose a novel network model, called Intra-class Structure Aware Networks (ISANs), to alleviate the difficulty of learning one single concept which exhibits in various forms. The proposed model introduces more neural units for the “defect” category rather than a single one, to accommodate the large variations in this category, which can significantly improve the representation power. Regularized by prior distribution of intra-class variants, our approach can learn intra-class structure of screen defect without extra fine-grained labels. Experimental results demonstrate that ISANs can effectively discriminate intra-class variants and gain significant performance improvement on screen defect detection task as well as the classification task in MNIST.
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
References
Bellman, R.: Adaptive Control Process: A Guided Tour. Princeton University Press, New Jersey (1961)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778. IEEE (2016)
Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: CVPR. IEEE (2017)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: NIPS, pp. 2017–2025. NIPS (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105. NIPS (2012)
Laptev, D., Savinov, N., Buhmann, J.M., Pollefeys, M.: Ti-POOLING: transformation-invariant pooling for feature learning in convolutional neural networks. In: CVPR, pp. 289–297. IEEE (2016)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: NIPS, pp. 396–404. NIPS (1990)
Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)
Qiao, S., Liu, C., Shen, W., Yuille, A.: Few-shot image recognition by predicting parameters from activations. arXiv preprint arXiv:1706.03466 (2017)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: NIPS, pp. 3859–3869. NIPS (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Strehl, A., Ghosh, J.: Cluster ensembles: a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)
Szegedy, C., et al.: Going deeper with convolutions. In: CVPR, pp. 1–9. IEEE (2015)
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)
Acknowledgments
This work is supported by National Natural Science Foundation of China (61572428,U1509206), Fundamental Research Funds for the Central Universities (2017FZA5014), National Key Research and Development Program (2016YFB1200203) and Key Research and Development Program of Zhejiang Province (2018C01004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, C., Song, J., Song, S., Luo, S., Sun, L., Song, M. (2018). Intra-class Structure Aware Networks for Screen Defect Detection. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-04212-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04211-0
Online ISBN: 978-3-030-04212-7
eBook Packages: Computer ScienceComputer Science (R0)