Abstract
As the recent success of deep neural networks solved many single domain tasks, next generation problems should be on multi-domain tasks. To its previous stage, we investigated how auxiliary information can affect the deep learning model. By setting the primary class and auxiliary classes, characteristics of deep learning models can be studied when the additional task is added to original tasks. In this paper, we provide a theoretical consideration on additional information and concluded that at least random information should not affect deep learning models. Then, we propose an architecture which is capable of ignoring redundant information and show this architecture practically copes well with auxiliary information. Finally, we propose some examples of auxiliary information which can improve the performance of our architecture.
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References
Cover, T.M., Thomas, J.A.: Elements of information theory. John Wiley & Sons (2012)
Forgy, E.W.: Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768–769 (1965)
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)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)
Jung, H., Lee, S., Yim, J., Park, S., Kim, J.: Joint fine-tuning in deep neural networks for facial expression recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2983–2991 (2015)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980
Li, Z., Hoiem, D.: Learning without forgetting. In: European Conference on Computer Vision, pp. 614–629. Springer (2016)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. Oakland, CA, USA (1967)
Ruder, S.: An overview of multi-task learning in deep neural networks (2017). arXiv:1706.05098
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization (2016). arXiv:1611.03530
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2017)
Acknowledgements
This work was supported by the ICCTDP (No. 10063172) funded by MOTIE, Korea.
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Seong, S., Lee, C., Kim, J. (2019). Improving Deep Neural Networks by Adding Auxiliary Information. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_4
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DOI: https://doi.org/10.1007/978-3-319-78452-6_4
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