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Review and experimental comparison of generative adversarial networks for synthetic image generation

    Gabriela Vdoviak Affiliation
    ; Henrikas Giedra Affiliation

Abstract

The application of machine learning algorithms has become widespread particularly in fields such as medicine, business, and commerce. However, achieving accurate classification results with these algorithms often relies on large-scale training datasets, making data collection a lengthy and complex process. This paper reviews the current utilization of generative adversarial network (GAN) architectures and discusses recent scientific research on their practical applications. The study emphasizes the significance of addressing data scarcity in the process of training the machine learning algorithms and highlights the potential of advanced GAN architectures, in particular StyleGAN2-ADA, to mitigate this challenge. The findings contribute to ongoing efforts aimed at enhancing the efficiency and applicability of artificial intelligence across diverse domains by presenting a viable solution to the constraint of limited training data for image classification tasks.

Keyword : computer vision, convolutional neural networks, deep learning, generative adversarial networks, image classification, image synthesis

How to Cite
Vdoviak, G., & Giedra, H. (2024). Review and experimental comparison of generative adversarial networks for synthetic image generation. New Trends in Computer Sciences, 2(1), 1–18. https://doi.org/10.3846/ntcs.2024.20516
Published in Issue
May 30, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ahmed, S. F., Bin Alam, M. S., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023). Deep learning modelling techniques: Current progress, applications, advantages, and challenges. Artificial Intelligence Review, 56(11), 13521–13617. https://doi.org/10.1007/S10462-023-10466-8

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), Article 53. https://doi.org/10.1186/s40537-021-00444-8

Borji, A. (2019). Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding, 179, 41–65. https://doi.org/10.1016/j.cviu.2018.10.009

Chakraborty, T., Reddy, U. K. S., Naik, S. M., Panja, M., & Manvitha, B. (2024). Ten years of generative adversarial nets (GANs): A survey of the state-of-the-art. Machine Learning: Science and Technology, 5(1), Article 011001. https://doi.org/10.1088/2632-2153/ad1f77

Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. arXiv. https://doi.org/10.48550/arXiv.1606.03657

Chen, Y., Yang, X.-H., Wei, Z., Heidari, A. A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., & Guan, Q. (2022). Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine, 144, Article 105382. https://doi.org/10.1016/j.compbiomed.2022.105382

Dash, A., Ye, J., & Wang, G. (2023). A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines: From medical to remote sensing. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3346273

Feng, Z., Daković, M., Ji, H., Zhou, X., Zhu, M., Cui, X., & Stanković, L. (2023). Interpretation of latent codes in InfoGAN with SAR images. Remote Sensing, 15(5), Article 1254. https://doi.org/10.3390/rs15051254

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139–144. https://doi.org/10.1145/3422622

Henry, J., Natalie, T., & Madsen, D. (2021). Pix2Pix GAN for image-to-image translation. ResearchGate. https://doi.org/10.13140/RG.2.2.32286.66887

Iglesias, G., Talavera, E., & Díaz-Álvarez, A. (2023). A survey on GANs for computer vision: Recent research, analysis and taxonomy. Computer Science Review, 48, Article 100553. https://doi.org/10.1016/j.cosrev.2023.100553

Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of GANs for improved quality, stability, and variation. ArXiv. https://doi.org/10.48550/arXiv.1710.10196

Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., & Aila, T. (2020). Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33, 12104–12114.

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). IEEE. https://doi.org/10.1109/CVPR.2019.00453

Mert, A. (2023). Enhanced dataset synthesis using conditional generative adversarial networks. Biomedical Engineering Letters, 13(1), 41–48. https://doi.org/10.1007/s13534-022-00251-x

Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. ArXiv. https://doi.org/10.48550/arXiv.1411.1784

Pérez, E., & Ventura, S. (2023). Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis. Artificial Intelligence in Medicine, 141, Article 102556. https://doi.org/10.1016/j.artmed.2023.102556

Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv. https://doi.org/10.48550/arXiv.1511.06434

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.

Saxena, D., & Cao, J. (2021). Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Computing Surveys (CSUR), 54(3), 1–42. https://doi.org/10.1145/3446374

Son, D. M., Kwon, H. J., & Lee, S. H. (2023). Enhanced night-to-day image conversion using CycleGAN-based base-detail paired training. Mathematics, 11(14), Article 3102. https://doi.org/10.3390/math11143102

Thamotharan, B., Sriram, A. L., & Sundaravadivazhagan, B. (2023). A comparative study of GANs (Text to Image GANs). In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), Lecture notes in networks and systems: Vol. 735. Proceedings of ICACTCE’23 – The International Conference on Advances in Communication Technology and Computer Engineering. ICACTCE 2023 (pp. 229–241). Springer. https://doi.org/10.1007/978-3-031-37164-6_16

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://doi.org/10.1145/3439723

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. N. (2017). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 5907–5915). IEEE. https://doi.org/10.1109/ICCV.2017.629

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2223–2232). IEEE. https://doi.org/10.1109/ICCV.2017.244