Open Access
Issue |
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
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Article Number | 03012 | |
Number of page(s) | 8 | |
Section | Topics in Computer Science | |
DOI | https://doi.org/10.1051/shsconf/202213903012 | |
Published online | 13 May 2022 |
- Kaneko, T., 2018. Generative adversarial networks: Foundations and applications. Acoustical Science and Technology, 39(3), pp.189-197. [Google Scholar]
- Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B. and Bharath, A.A., 2018. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), pp.53-65. [CrossRef] [Google Scholar]
- Luo, W., Wang, P., Wang, J. and An, W., 2019, March. The research process of generative adversarial networks. In Journal of Physics: Conference Series (Vol. 1176, No. 3, p. 032008). IOP Publishing. [CrossRef] [Google Scholar]
- Hong, Y., Hwang, U., Yoo, J. and Yoon, S., 2019. How generative adversarial networks and their variants work: An overview. ACM Computing Surveys (CSUR), 52(1), pp.1-43. [Google Scholar]
- Alqahtani, H., Kavakli-Thorne, M. and Kumar, G., 2019. Applications of generative adversarial networks (gans): An updated review. Archives of Computational Methods in Engineering, pp.1-28. [Google Scholar]
- Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F. and Zheng, Y., 2019. Recent progress on generative adversarial networks (GANs): A survey. IEEE Access, 7, pp.36322-36333. [Google Scholar]
- Salehi, P., Chalechale, A. and Taghizadeh, M., 2020. Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments. arXiv preprint arXiv:2005.13178. [Google Scholar]
- Lee, M. and Seok, J., 2020. Regularization methods for generative adversarial networks: An overview of recent studies. arXiv preprint arXiv:2005.09165. [Google Scholar]
- Cheng, J., Yang, Y., Tang, X., Xiong, N., Zhang, Y. and Lei, F., 2020. Generative Adversarial Networks: A Literature Review. KSII Transactions on Internet & Information Systems, 14(12). [Google Scholar]
- Kumar, M.P. and Jayagopal, P., 2020. Generative adversarial networks: a survey on applications and challenges. International Journal of Multimedia Information Retrieval, pp.1-24. [Google Scholar]
- Dash, A., Ye, J. and Wang, G., 2021. A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines--From Medical to Remote Sensing. arXiv preprint arXiv:2110.01442. [Google Scholar]
- Jabbar, A., Li, X. and Omar, B., 2021. A survey on generative adversarial networks: Variants, applications, and training. ACM Computing Surveys (CSUR), 54(8), pp.1-49 [Google Scholar]
- Li, Y., Wang, Q., Ouyang, W. and Hu, L., 2021. The theoretical research of generative adversarial networks: an overview. Neurocomputing 435, pp 26-41. [CrossRef] [Google Scholar]
- Aggarwal, A., Mittal, M. and Battineni, G., 2021. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, p.100004. [CrossRef] [Google Scholar]
- Alqahtani, H., Kavakli-Thorne, M. and Liu, C.Z., 2019. An introduction to person re-identification with generative adversarial networks. arXiv preprint arXiv:1904.05992. [Google Scholar]
- Jin, L., Tan, F. and Jiang, S., 2020. Generative adversarial network technologies and applications in computer vision. Computational Intelligence and Neuroscience, 2020. [Google Scholar]
- Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K.K., Prabowo, A., Rahaman, M.S. and Salim, F.D., 2020. Generative adversarial networks for spatio-temporal data: A survey. arXiv preprint arXiv:2008.08903. [Google Scholar]
- Sampath, V., Maurtua, I., Martín, J.J.A. and Gutierrez, A., 2021. A survey on generative adversarial networks for imbalance problems in computer vision tasks. Journal of big Data, 8(1), pp.1-59. [CrossRef] [Google Scholar]
- Toshpulatov, M., Lee, W. and Lee, S., 2021. Generative adversarial networks and their application to 3D face generation: A survey. Image and Vision Computing, p.104119. [CrossRef] [Google Scholar]
- Wang, Z., She, Q. and Ward, T.E., 2021. Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2), pp.1-38. [CrossRef] [Google Scholar]
- Park, S.W., Ko, J.S., Huh, J.H. and Kim, J.C., 2021. Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications. Electronics, 10(10), p.1216. [CrossRef] [Google Scholar]
- Hajarolasvadi, N., Ramírez, M.A., Beccaro, W. and Demirel, H., 2020, Generative adversarial networks in human emotion synthesis: A review. IEEE Access, 8, pp.218499-218529. [CrossRef] [Google Scholar]
- Wang, L., Chen, W., Yang, W., Bi, F. and Yu, F.R., 2020. A state-of-the-art review on image synthesis with generative adversarial networks. IEEE Access, 8, pp.63514-63537. [CrossRef] [Google Scholar]
- Tyagi, S. and Yadav, D., 2021. A Comprehensive Review on Image Synthesis with Adversarial Networks: Theory, Literature, and Applications. Archives of Computational Methods in Engineering, pp.1-21. [Google Scholar]
- Shamsolmoali, P., Zareapoor, M., Granger, E., Zhou, H., Wang, R., Celebi, M.E. and Yang, J., 2021. Image synthesis with adversarial networks: A comprehensive survey and case studies. Information Fusion. [Google Scholar]
- Noor, N.A.N.M. and Suaib, N.M., 2020, May. Facial Expression Transfer using Generative Adversarial Network: A Review. In IOP Conference Series: Materials Science and Engineering (Vol. 864, No. 1, p. 012077). IOP Publishing. [CrossRef] [Google Scholar]
- Zhua, M., Gongb, S., Qianb, Z. and Zhanga, L., 2019. A Brief Review on Cycle Generative Adversarial Networks. Proceedings of the 7th IIAE International Conference on Intelligent Systems and Image Processings 2019, pp 235-242. [Google Scholar]
- Alotaibi, A., 2020. Deep generative adversarial networks for image-to-image translation: A review. Symmetry, 12(10), p.1705. [Google Scholar]
- Jam, J., Kendrick, C., Walker, K., Drouard, V., Hsu, J.G.S. and Yap, M.H., 2020. A comprehensive review of past and present image inpainting methods. Computer Vision and Image Understanding, p.103147. [Google Scholar]
- Kumar, A., 2021. Super-Resolution with Deep Learning Techniques: A Review. Computational Intelligence Methods for Super-Resolution in Image Processing Applications, pp.43-59. [Google Scholar]
- Agnese, J., Herrera, J., Tao, H. and Zhu, X., 2020. A survey and taxonomy of adversarial neural networks for text-to-image synthesis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(4), p.e1345. [CrossRef] [Google Scholar]
- Frolov, S., Hinz, T., Raue, F., Hees, J. and Dengel, A., 2021. Adversarial text-to-image synthesis: A review. arXiv preprint arXiv:2101.09983. [Google Scholar]
- Lei, Y., Qiu, R.L., Wang, T., Curran, W.J., Liu, T. and Yang, X., 2020. Generative Adversarial Network for Image Synthesis. arXiv preprint arXiv:2012.15446. [Google Scholar]
- Lan, L., You, L., Zhang, Z., Fan, Z., Zhao, W., Zeng, N., Chen, Y. and Zhou, X., 2020. Generative adversarial networks and its applications in biomedical informatics. Frontiers in Public Health, 8, p.164. [CrossRef] [Google Scholar]
- Koshino, K., Werner, R.A., Pomper, M.G., Bundschuh, R.A., Toriumi, F., Higuchi, T. and Rowe, S.P., 2021. Narrative review of generative adversarial networks in medical and molecular imaging. Annals of Translational Medicine, 9(9). [Google Scholar]
- Singh, N.K. and Raza, K., 2021. Medical Image Generation Using Generative Adversarial Networks: A Review. Health Informatics: A Computational Perspective in Healthcare, pp.77-96. [Google Scholar]
- Yang, G., Lv, J., Chen, Y., Huang, J. and Zhu, J., 2021. Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging--Mini Review, Comparison and Perspectives. arXiv preprint arXiv:2105.01800. [Google Scholar]
- Suganthi, K., 2021. Review of Medical Image Synthesis using GAN Techniques. In ITM Web of Conferences (Vol. 37, p. 01005), EDP Sciences. [CrossRef] [EDP Sciences] [Google Scholar]
- Shin, Y., Yang, J. and Lee, Y.H., 2021. Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging. Radiology: Artificial Intelligence, 3(3), p.e200157 [CrossRef] [Google Scholar]
- Yi, X., Walia, E. and Babyn, P., 2019. Generative adversarial network in medical imaging: A review. Medical image analysis, 58, p.101552. [CrossRef] [Google Scholar]
- Gopal, A., Gandhimaruthian, L. and Ali, J., 2020. Role of General Adversarial Networks in Mammogram Analysis: A Review. Current Medical Imaging, 16(7), pp.863-877. [CrossRef] [Google Scholar]
- Tschuchnig, M.E., Oostingh, G.J. and Gadermayr, M., 2020. Generative adversarial networks in digital pathology: a survey on trends and future potential. Patterns, 1(6), p.100089. [CrossRef] [Google Scholar]
- Logan, R., Williams, B.G., Ferreira da Silva, M., Indani, A., Schcolnicov, N., Ganguly, A. and Miller, S.J., 2021. Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification. Frontiers in aging neuroscience, p.497. [Google Scholar]
- Jose, L., Liu, S., Russo, C., Nadort, A. and Di Ieva, A., 2021. Generative adversarial networks in digital pathology and histopathological image processing: A review. Journal of Pathology Informatics, 12. [Google Scholar]
- Skandarani, Y., Lalande, A., Afilalo, J. and Jodoin, P.M., 2021. Generative adversarial networks in cardiology. Canadian Journal of Cardiology. Elsevier. [Google Scholar]
- Wali, A., Alamgir, Z., Karim, S., Fawaz, A., Ali, M.B., Adan, M. and Mujtaba, M., 2022. Generative adversarial networks for speech processing: A review. Computer Speech & Language, 72, p.101308. [CrossRef] [Google Scholar]
- Aldausari, N., Sowmya, A., Marcus, N. and Mohammadi, G., 2020. Video Generative Adversarial Networks: A Review. arXiv preprint arXiv:2011.02250. [Google Scholar]
- Akanksha, S., Neeru, J. and Rana, P.S., 2020. Potential of generative adversarial net algorithms in image and video processing applications–a survey. Multimedia Tools and Applications, 79(37-38), pp.27407-27437. [CrossRef] [Google Scholar]
- Wang, J., Cheng, M., Wu, P. and Chen, B., 2019. A survey on digital image steganography. Journal of Information Hiding and Privacy Protection, 1(2), p.87. [CrossRef] [Google Scholar]
- Liu, J., Ke, Y., Zhang, Z., Lei, Y., Li, J., Zhang, M. and Yang, X., 2020. Recent advances of image steganography with generative adversarial networks. IEEE Access, vol. 8, pp. 60575-60597. [CrossRef] [Google Scholar]
- Subramanian, N., Elharrouss, O., Al-Maadeed, S. and Bouridane, A., 2021. Image Steganography: A Review of the Recent Advances. IEEE Access. [Google Scholar]
- Arora, A. and Shantanu, 2020. A Review on Application of GANs in Cybersecurity Domain. IETE Technical Review, pp.1-9. [CrossRef] [Google Scholar]
- Yinka-Banjo, C. and Ugot, O.A., 2020. A review of generative adversarial networks and its application in cybersecurity. Artificial Intelligence Review, 53(3), pp.1721-1736. [CrossRef] [Google Scholar]
- Navidan, H., Moshiri, P.F., Nabati, M., Shahbazian, R., Ghorashi, S.A., Shah-Mansouri, V. and Windridge, D., 2021. Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks, p.108149. [CrossRef] [Google Scholar]
- Bourou, S., El Saer, A., Velivassaki, T.H., Voulkidis, A. and Zahariadis, T., 2021. A Review of Tabular Data Synthesis Using GANs on an IDS Dataset. Information, 12(9), p.375. [CrossRef] [Google Scholar]
- Lin, Y. and Parvataneni, K., 2021. Deepfake Generation, Detection, and Use Cases: A Review Paper. International Journal of Computational and Biological Intelligent Systems, 3(2). [Google Scholar]
- Revi, K.R., Vidya, K.R. and Wilscy, M., 2021. Detection of Deepfake Images Created Using Generative Adversarial Networks: A Review. In Second International Conference on Networks and Advances in Computational Technologies (pp. 25-35). Springer, [CrossRef] [Google Scholar]
- Mridha, M.F., Keya, A.J., Hamid, M.A., Monowar, M.M. and Rahman, M.S., 2021. A Comprehensive Review on Fake News Detection with Deep Learning. IEEE Access. [Google Scholar]
- Eckerli, F., 2021. Generative Adversarial Networks in finance: an overview. Available at SSRN 3864965. [Google Scholar]
- Speak with signs: Active learning platform for Greek Sign Language, English Sign Language, and their translation, Maria Papatsimouli, Lazaros Lazaridis, Konstantinos-Filippos Kollias, Ioannis Skordas, George F. Fragulis, SHS Web Conf. 102 01008 (2021), DOI: 10.1051/shsconf/202110201008 [Google Scholar]
- Lazaridis, L., Papatsimouli, M., Kollias, K. F., Sarigiannidis, P., & Fragulis, G. F. (2021, July). Hitboxes: A Survey About Collision Detection in Video Games. In International Conference on Human-Computer Interaction (pp. 314-326). Springer, Cham. [Google Scholar]
- Kollias, K.-F.; Syriopoulou-Delli, C.K.; Sarigiannidis, P.; Fragulis, G.F. The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review. Electronics 2021, 10, 2982. [CrossRef] [Google Scholar]
- Fragulis, G. F., Papatsimouli, M., Lazaridis, L., & Skordas, I. A. (2021). An Online Dynamic Examination System (ODES) based on open source software tools. Software Impacts, 7, 100046. [CrossRef] [Google Scholar]
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