Abstract
Convolutional neural network (CNN) have been extensively applied for a variety of tasks. However, the internal processes of hidden units in solving problems are mostly unknown. In this study, we presented the use of canonical correlation analysis (CCA) to understand the information flow of the hidden layers in CNN. The proposed method analyzed and compared the information flow by measuring the correlations between a given feature vector and the activation pattern at each layer of the CNN. We quantified and analyzed specific information flows using the CCA to examine how the architecture works in the two experiments. In the first experiment, we analyzed the information flow of the U-net and auto-encoder architectures to remove the distorted light source information, and showed that the U-net works more efficiently for this task. In the second experiment, we analyzed the information flow of the architecture used for multitask learning, in which the classification of shifted characters in images and the estimation of the shift amount are performed simultaneously, and showed that it performed properly according to the task.
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References
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Hunt, R.: The Reproduction of Colour. Wiley, New York (2004)
Lam, E.: Combining gray world and retinex theory for automatic white balance in digital photography. In: ISCE, pp. 134–139 (2005)
Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)
Nikitenko, D., Wirth, M., Trudel, K.: Applicability of white-balancing algorithms to restoring faded colour slides: an empirical evaluation. J. Multimedia 3(5), 9–18 (2008)
Bianco, S., Cusano, C., Schettini, R.: Color constancy using CNNs. In: CVPR Workshops, pp. 81–89 (2015)
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: SUN database: large-scale scene recognition from abbey to zoo. In: CVPR (2010)
Cheng, D., Prasad, D.K., Brown, M.S.: Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution. J. Opt. Soc. Am. A 31(5), 1049–1058 (2014)
Lam, H.-K., Au, O.C., Wong, C.-W.: Automatic white balancing using luminance component and standard deviation of RGB components. In: ICASSP, pp. 493–496 (2004)
Rizzi, A., Gatta, C., Marini, D.: A new algorithm for unsupervised global and local color correction. Pattern Recogn. Lett. 24(11), 1663–1677 (2003)
Hotelling, H.: Relations between two sets of variates. Biometrica 28(3–4), 321–377 (1936)
Hidaka, A., Kurita, T.: Consecutive dimensionality reduction by canonical correlation analysis for visualization of convolutional neural networks. In: Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications, pp. 160–167 (2017)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(11), 559–572 (1901)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)
van der Maaten, L., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
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This work was partly supported by JSPS KAKENHI Grant Number 16K00239.
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Kanda, K., Kavitha, M., Miyao, J., Kurita, T. (2020). Analysis of Information Flow in Hidden Layers of the Trained Neural Network by Canonical Correlation Analysis. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_16
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DOI: https://doi.org/10.1007/978-981-15-4818-5_16
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