№1, 2022


Yadigar N. Imamverdiyev, Firangiz I. Musayeva

Recently, a lot of research has been done on the use of generative models in the field of computer vision and image classification. At the same time, effective work has been done with the help of an environment called generative adversarial networks, such as video generation, music generation, image synthesis, text-to-image conversion. Generative adversarial networks are artificial intelligence algorithms designed to solve the problems of generative models. The purpose of the generative model is to study the set of training patterns and their probable distribution. The article discusses generative adversarial networks, their types, problems, and advantages, as well as classification and regression, segmentation of medical images, music generation, best description capabilities, text image conversion, video generation, etc. general information is given. In addition, comparisons were made between the generative adversarial network algorithms analyzed on some criteria (pp.20-27).

Keywords: Neural networks, Generative models, Generative adversarial networks, Auto encoders, Generator, Discriminator
  • Alguliyev, R. M., Abdullayeva, F. J., & Ojagverdiyeva, S. S. (2020). Protecting children on the internet using deep generative adversarial networks. International Journal of Computational Systems Engineering, 6(2), 84-90. https://ieeexplore.ieee.org/document/771073
  • Antipov, G., Baccouche, M., & Dugelay, J. L. (2017, September). Face aging with conditional generative adversarial networks. In 2017 IEEE international conference on image processing (ICIP) (pp. 2089-2093). IEEE.
  • Arjovsky, M., & Bottou, L. (2017). Towards principled methods for training generative adversarial networks. https://openreview.net/pdf?id=Hk4_qw5xe  Accessed 11 December 2021.
  • Arjovsky, M., Chintala, S., & Bottou, L. (2017, July). Wasserstein generative adversarial networks. In International conference on machine learning (pp. 214-223). PMLR.
  • Bang, D., & Shim, H. (2018, July). Improved training of generative adversarial networks using representative features. In International conference on machine learning (pp. 433-442). PMLR.
  • Che, T., Li, Y., Jacob, A. P., Bengio, Y., & Li, W. (2016). Mode regularized generative adversarial networks. https://arxiv.org/abs/1612.02136 Accessed 11 December 2021.
  • Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016, December). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Proceedings of the 30th International Conference on Neural Information Processing Systems (pp. 2180-2188).
  • Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: an overview. IEEE Signal Process Mag 35 (1): 53–65. https://arxiv.org/abs/1710.07035 Accessed 11 December 2021.
  • Donahue, J., Krähenbühl, P., & Darrell, T. (2016). Adversarial feature learning. https://arxiv.org/abs/1605.09782 Accessed 11 December 2021.
  • Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., & Courville, A. (2016). Adversarially learned inference. https://arxiv.org/abs/1606.00704 Accessed 11 December 2021.
  • Fedus, W., Rosca, M., Lakshminarayanan, B., Dai, A. M., Mohamed, S., & Goodfellow, I. (2017). Many paths to equilibrium: GANs do not need to decrease a divergence at every step. https://arxiv.org/abs/1710.08446 Accessed 11 December 2021.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139144. https://doi.org/10.1145/3422622
  • Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of wasserstein gans. https://arxiv.org/abs/1704.00028 Accessed 11 December 2021.
  • Hitawala, S. (2018). Comparative study on generative adversarial networks. https://arxiv.org/abs/1801.04271 Accessed 11 December 2021.
  • Xue, Y., Xu, T., Zhang, H., Long, L. R., & Huang, X. (2018). Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation. Neuroinformatics, 16(3), 383-392. https://link.springer.com/article/10.1007%2Fs12021-018-9377-x
  • Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134).
  • Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. https://arxiv.org/abs/1710.10196  Accessed 11 December 2021.
  • Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401-4410).
  • Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690).
  • Lucic, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2017). Are gans created equal? a large-scale study. https://arxiv.org/abs/1711.10337v4 Accessed 11 December 2021.
  • Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., & Paul Smolley, S. (2017). Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2794-2802).
  • Metz, L., Poole, B., Pfau, D., & Sohl-Dickstein, J. (2016). Unrolled generative adversarial networks. https://arxiv.org/abs/1611.02163 Accessed 11 December 2021.
  • Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. https://arxiv.org/abs/1411.1784 Accessed 11 December 2021.
  • Nowozin, S., Cseke, B., & Tomioka, R. (2016, December). f-gan: Training generative neural samplers using variational divergence minimization. In Proceedings -of the 30th International Conference on Neural Information Processing Systems (pp. 271-279).
  • Odena, A., Olah, C., & Shlens, J. (2017, July). Conditional image synthesis with auxiliary classifier gans. In International conference on machine learning (pp. 2642-2651). PMLR.
  • Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. https://arxiv.org/pdf/1511.06434.pdf%5D Accessed 11 December 2021.
  • Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. Advances in neural information processing systems, 29, 2234-2242. https://proceedings.mlr.press/v80/bang18a/bang18a.pdf
  • Theis, L., Oord, A. V. D., & Bethge, M. (2015). A note on the evaluation of generative models. https://arxiv.org/pdf/1511.01844.pdf Accessed 11 December 2021.
  • Tran, L., Yin, X., & Liu, X. (2017). Disentangled representation learning gan for pose-invariant face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1415-1424).
  • Tulyakov, S., Liu, M. Y., Yang, X., & Kautz, J. (2018). Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1526-1535).
  • Wang, Z., She, Q., & Ward, T. E. (2021). Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2), 1-38. https://arxiv.org/pdf/1906.01529.pdf
  • Warde-Farley, D., & Bengio, Y. (2016). Improving generative adversarial networks with denoising feature matching. https://openreview.net/forum?id=S1X7nhsxl
  • Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., ... & Wang, G. (2018). Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE transactions on medical imaging, 37(6), 1348-1357. https://arxiv.org/pdf/1708.00961.pdf
  • Yeh, R. A., Chen, C., Yian Lim, T., Schwing, A. G., Hasegawa-Johnson, M., & Do, M. N. (2017). Semantic image inpainting with deep generative models. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5485-5493).
  • Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. N. (2018). Stackgan++: Realistic image synthesis with stacked generative adversarial networks. IEEE transactions on pattern analysis and machine intelligence, 41(8), 1947-1962. https://arxiv.org/abs/1710.10916