Abstract
An alternative training model is proposed for adversarial networks to correct a slightly defective data. Generator is first acquired by classical Generative Adversarial Networks, where the discriminator is trained only by feasible data. Then, both an encoder as the inverse mapping of the generator and a classifier which judges a feasibility of a generated data, are trained to lead the generator to correct an infeasible data by the minimum modification. The proposed method is applied to a housing member placement problem to satisfy every constraint for earthquake resistance, and evaluated by a rigorous structural calculation.
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References
Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Advances in Neural Information Processing Systems (2014)
Arjovsky, M., Chintala, S. and Bottou, L.: Wasserstein GAN. arXiv:1701.07875v2 (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017)
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv:1512.09300 (2016)
Rosca, M., Lakshminarayanan, B., Warde-Farley, D., Mohamed, S.: Variational Approaches for Auto-encoding Generative Adversarial Networks. arXiv:1706.04987 (2017)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial Feature Learning. arXiv:1605.09782v7 (2017)
Dumoulin, V., et al.: Adversarially Learned Inference. arXiv:1606.00704v3 (2017)
Metz, D., Poole, B., Pfau, D., Sohl-Dickstein, J.: Unrolled Generative Adversarial Networks. arXiv:1611.02163v4 (2017)
Lipton, Z.C., Tripathi, S.: Precise Recovery of Latent Vectors from Generative Adversarial Networks. arXiv:1702.04782v2 (2017)
Yoshitomi, S., Nakagawa, D., Sada, T.: Research on structural optimization for steel industrialised housing. J. Struct. Constr. Eng. 80(714), 1347–1355 (2015)
Zhang, H., et al.: Stack GAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. arXiv:1612.03242 (2016)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
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Ueda, T., Seo, M., Nishikawa, I. (2018). Data Correction by a Generative Model with an Encoder and its Application to Structure Design. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_40
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DOI: https://doi.org/10.1007/978-3-030-01424-7_40
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