Abstract
Breathtaking improvement has been seen with the recently proposed deep Generative Adversarial Network (GAN). Purposes of most existing GAN-based models majorly concentrate on generating realistic and vivid patterns by a pattern generator with the aid of the binary discriminator. However, few study were related to the promotion of classification performance with merits of those generated ones. In this paper, a novel and generalized classification framework called Style Neutralization Generative Adversarial Classifier (SN-GAC), based on the GAN framework, is introduced to enhance the classification accuracy by neutralizing possible inconsistent style information existing in the original data. In the proposed model, the generator of SN-GAC is trained by mapping the original patterns with certain styles (source) to their style-neutralized or standard counterparts (standard-target), capable of generating the targeted style-neutralized one (generated-target). On the other hand, pairs of both standard (source + standard-target) and generated (source + generated-target) patterns are fed into the discriminator, optimized by not only distinguishing between real and fake, but also classifying the input pairs with correct class label assignment. Empirical experiments fully demonstrate the effectiveness of the proposed SN-GAC framework by achieving so-far the highest accuracy on two benchmark classification databases including the face and the Chinese handwriting character, outperforming several relevant state-of-the-art baseline approaches.
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Notes
- 1.
The discriminator with auxiliary classifier is termed as D-C in this paper since it differs from the D of traditional GAN as in [5]. Moreover, the proposed D-C is also different from [16] since the classifier in the SN-GAC model can be directly applied for normal classification after well trained. However, the auxiliary classifier in [16] is only utilized to provide supervising information for better GAN training.
- 2.
The proposed SN-GAC model is evaluated only with dataset specifying groups of style patterns in this paper for the simplification purpose.
- 3.
Paired input is not evaluated for conventional baselines in Sect. 3 since style-neutralization cannot be achieved with traditional approaches.
References
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M.: TensorFlow: a system for large-scale machine learning. OSDI 16, 265–283 (2016)
Cate, H., Dalvi, F., Hussain, Z.: DeepFace: face generation using deep learning. arXiv preprint arXiv:1701.01876 (2017)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109–117. ACM (2004)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Gourier, N., Hall, D., Crowley, J.L.: Estimating face orientation from robust detection of salient facial structures. In: FG Net Workshop on Visual Observation of Deictic Gestures, vol. 6, p. 7 (2004)
Huang, K.Z., Yang, H., King, I., Lyu, M.R.: Machine Learning: Modeling Data Locally and Globally. Springer Science, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79452-3
Huang, K., Jiang, H., Zhang, X.Y.: Field support vector machines. IEEE Trans. Emerg. Top. Comput. Intell. 1(6), 454–463 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)
Jiang, H., Huang, K., Zhang, R.: Field support vector regression. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) International Conference on Neural Information Processing. LNCS, pp. 699–708. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-70087-8_72
Jiang, Y., Lian, Z., Tang, Y., Xiao, J.: DCFont: an end-to-end deep Chinese font generation system. In: SIGGRAPH Asia 2017 Technical Briefs, p. 22. ACM (2017)
Jing, X.Y., Wong, H.S., Zhang, D.: Face recognition based on 2D fisherface approach. Pattern Recognit. 39(4), 707–710 (2006)
Kim, T.: GitHub dcgan-tensorflow (2016). https://github.com/carpedm20/DCGAN-tensorflow
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)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: CASIA online and offline Chinese handwriting databases. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 37–41. IEEE (2011)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. arXiv preprint arXiv:1610.09585 (2016)
Sarkar, P., Nagy, G.: Style consistent classification of isogenous patterns. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 88–98 (2005)
Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200 (2016)
Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Comput. 12(6), 1247–1283 (2000)
Tian, Y.: GitHub zi2zi-tensorflow (2017). https://kaonashi-tyc.github.io/2017/04/06/zi2zi.html
Zhang, X.Y., Huang, K., Liu, C.L.: Pattern field classification with style normalized transformation. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p. 1621 (2011)
Acknowledgements
The work reported here was partially supported by the following: National Natural Science Foundation of China under grant no. 61473236; Natural Science Fund for Colleges and Universities in Jiangsu Province under grant no.17KJD520010; Suzhou Science and Technology Program under grant no. SYG201712, SZS201613; Jiangsu University Natural Science Research Programme under grant no. 17KJB-520041; Key Program Special Fund in XJTLU (KSF-A-01)
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Jiang, H., Huang, K., Zhang, R., Hussain, A. (2018). Style Neutralization Generative Adversarial Classifier. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_1
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