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2020 | OriginalPaper | Chapter

Development of the Tools for Research of Transformation of Digital Objects Based on Generative Adversarial Networks

Authors : Alexander Kadan, Maria Kadan

Published in: Modern Information Technology and IT Education

Publisher: Springer International Publishing

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Abstract

Generating models are a modern, rapidly developing direction of research in the application of neural network technologies. Its currently known applications include, in particular, image visualization, drawing, image resolution improvement, structured prediction, research in the field of training and data preprocessing for neural networks in cases where the production of test data is an unacceptably expensive process. They are actively discussing their capabilities in the tasks of providing information and computer security, tasks of computer technical expertise, tasks of transforming and restoring objects by fragments or when the information is extremely noisy.

Generating models are a modern rapidly developing field of research in neural network technologies. Its applications include image visualization, drawing, image resolution improvement, structured prediction, research in the field of providing information security, tasks of computer technical expertise, tasks of transforming and restoring fragmented or is extremely noisy objects. The aim of the study is to create a software product that tests the hypothesis of the successful use of generative adversarial networks (GAN) to improve the characteristics of images. The use of GAN allows solving the problem of correcting distorted digital objects not by traditional adjustment of their individual parameters, but by generating undistorted objects that are “indistinguishable” from the original ones.

The article presents and analyzes the method of building a neural network that is capable to remove noise from images, preventing them from being too blurred and retaining clarity (in comparison with the original), which proves its ability to generalize. The experiment showed that GAN with sufficient efficiency not only removes noise from images with a distorting signal on which it was trained, but also in the case of noise unknown to the etymology network. The success of training to suppress specific distortions of digital graphic objects makes it possible to assume that by training a neural network to work with other types of distortion, one can achieve successful results for solving a wider class of problems.

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Simoncelli, E., Olshausen, B.: Natural image statistics and neural representation. Ann. Rev. Neurosci. 24, 1193–1216 (2001). https://doi.org/10.1146/annurev.neuro.24.1.1193CrossRef

Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. arXiv: 1312.6114 (2014). https://arxiv.org/abs/1312.6114. Accessed 25 Sept 2018

Rezende, D., Mohamed, S., Wierstra, D.: Stochastic Backpropagation and Approximate Inference in Deep Generative Models. arXiv: 1401.4082 (2014). https://arxiv.org/abs/1401.4082. Accessed 25 Sept 2018

Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv: 1406.2661 (2014). https://arxiv.org/abs/1406.2661. Accessed 25 Sept 2018

Rezende, D., Mohamed, S.: Variational Inference with Normalizing Flows. arXiv: 1505.05770 (2015). https://arxiv.org/abs/1505.05770. Accessed 25 Sept 2018

Kingma, D.P., Salimans, T., Welling, M.: Improving variational inference with inverse autoregressive flow Flows. arXiv: 1606.04934 (2016). https://arxiv.org/abs/1606.04934. Accessed 25 Sept 2018

Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. arXiv: 1605.08803 (2016). https://arxiv.org/abs/1605.08803. Accessed 25 Sept 2018

Denton, E.L., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a laplacian pyramid of adversarial networks. arXiv: 1506.05751 (2015). https://arxiv.org/abs/1506.05751. Accessed 25 Sept 2018

Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv: 1511.06434 (2015). https://arxiv.org/abs/1511.06434. Accessed 25 Sept 2018

10.

Uehara, M., Sato, I., Suzuki, M., Nakayama, K., Matsuo, Y.: Generative Adversarial Nets from a Density Ratio Estimation Perspective. arXiv: 1610.02920 (2016). https://arxiv.org/abs/1610.02920. Accessed 25 Sept 2018

11.

Mohamed, S., Lakshminarayanan, B.: Learning in implicit generative models. arXiv: 1610.03483 (2016). https://arxiv.org/abs/1610.03483. Accessed 25 Sept 2018

12.

Odena, A., Olah, C., Shlens, J.: Conditional Image Synthesis with Auxiliary Classifier GANs. arXiv: 1610.09585 (2016). https://arxiv.org/abs/1610.09585. Accessed 25 Sept 2018

13.

Balle, J., Laparra, V., Simoncelli, E.P.: Density modeling of images using a generalized normalization transformation. arXiv: 1511.06281 (2015). https://arxiv.org/abs/1511.06281. Accessed 25 Sept 2018

14.

Toderici, G., et al.: Full resolution image compression with recurrent neural networks. arXiv: 1608.05148 (2016). https://arxiv.org/abs/1608.05148. Accessed 25 Sept 2018

15.

van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. arXiv: 1601.06759 (2016). https://arxiv.org/abs/1601.06759. Accessed 25 Sept 2018

16.

van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., Kavukcuoglu, K.: Conditional image generation with pixelcnn decoders. arXiv: 1606.05328 (2016). https://arxiv.org/abs/1606.05328. Accessed 25 Sept 2018

17.

Ledig, C., et al.: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv: 1609.04802 (2016). https://arxiv.org/abs/1609.04802. Accessed 25 Sept 2018

18.

Kingma, D.P., Rezende, D.J., Mohamed, S., Welling, M.: Semi-supervised learning with deep generative models. arXiv: 1406.5298 (2014). https://arxiv.org/abs/1406.5298. Accessed 25 Sept 2018

19.

Blundell, C., et al.: Model-Free Episodic Control. arXiv: 1606.04460 (2016). https://arxiv.org/abs/1606.04460. Accessed 25 Sept 2018

20.

Liou, C.-Y., Cheng, C.-W., Liou, J.-W., Liou, D.-R.: Autoencoder for words. Neurocomputing 139, 84–96 (2014). https://doi.org/10.1016/j.neucom.2013.09.055CrossRef

21.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). https://doi.org/10.1126/science.1127647MathSciNetCrossRefMATH

22.

Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv: 1703.10593 (2017). https://arxiv.org/abs/1703.10593. Accessed 25 Sept 2018

23.

Maas, A. L., Hannun, A.Y., Ng. A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, JMLR: W&CP, vol. 28 (2013). https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf. Accessed 25 Sept 2018

24.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770–778 (2016). https://doi.org/10.1109/cvpr.2016.90

25.

Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv: 1412.6980 (2014). https://arxiv.org/abs/1412.6980. Accessed 25 Aug 2018

Title: Development of the Tools for Research of Transformation of Digital Objects Based on Generative Adversarial Networks
Authors: Alexander Kadan
Maria Kadan
Publisher: Springer International Publishing
Book: Modern Information Technology and IT Education
Print ISBN: 978-3-030-46894-1

Electronic ISBN: 978-3-030-46895-8

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-46895-8_18

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