Challenges, Corresponding Solutions, and Applications of Generative Adversarial Networks
DOI:
https://doi.org/10.54097/hset.v57i.9991Keywords:
Artificial intelligence, Generative Adversarial Network, Corresponding solutions.Abstract
Generative adversarial networks (GANs) have become a popular deep learning framework in the field of artificial intelligence. Researchers have developed various types of GANs, such as Conditional GANs (CGANs), Mode Dropping GANs (MDGANs), and Wasserstein GANs (WGANs), which have been applied to many different fields. Despite the success of GANs in various tasks, there are still some challenges that need to be addressed. For example, model collapse, non-convergence, and diminished gradient could hinder the training of GANs. In this paper, the authors first introduce the basics of GANs before highlighting a few common problems associated with GANs and their corresponding solutions. For instance, they discuss techniques such as mini-batch discrimination and batch normalization that can help mitigate model collapse and diminish gradient problems. Additionally, they cover methods that can improve the convergence rate and stability of GANs, such as using alternative loss functions and incorporating regularization algorithms. Finally, this paper briefly introduces some recent applications of GANs in image generation, video prediction, and other areas. Despite the challenges that still exist, GANs have shown great promise in various fields, and with further research, GANs have the potential to become more robust and efficient models for generating high-quality synthetic data.
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References
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv preprint arXiv:1406.2661.
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 214-223). PMLR.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028.
Goodfellow, I. J. (2016). NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160.
Saxena, D., & Cao, J. (2022). Generative adversarial networks (GANs): Challenges, solutions, and future directions. ACM Computing Surveys (CSUR), 54(3), 63.
Tseng, H. Y., Jiang, L., Liu, C., Yang, M. H., & Yang, W. (2021). Regularizing generative adversarial networks under limited data.
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., & Aila, T. (2020). Training generative adversarial networks with limited data. In Advances in Neural Information Processing Systems (pp. 7124-7135).
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training GANs.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015). Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567.
Laine, S., & Aila, T. (2017). Temporal ensembling for semi-supervised learning. In International Conference on Learning Representations.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W., Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., & Loy, C. C. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European Conference on Computer Vision (ECCV).
Ding, Z., Liu, X. Y., Yin, M., Liu, W., & Kong, L. (2019). Tgan: Deep tensor generative adversarial nets for large image generation. arXiv preprint arXiv:1901.09953.
Yang, J., Kannan, A., Batra, D., & Parikh, D. (2017). LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation.
Yu, L., Zhang, W., Wang, J., & Yu, Y. (2017). SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient.
Benhenda, M. (2017). ChemGAN challenge for drug discovery: Can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.
Dai, W., Doyle, J., Liang, X., Zhang, H., Dong, N., Li, Y., & Xing, E. P. (2017). Scan: Structure correcting adversarial network for chest xrays organ segmentation. arXiv preprint arXiv:1703.08770.
Hwang, J. J., Azernikov, S., Efros, A. A., & Yu, S. X. (2018). Learning beyond human expertise with generative models for dental restorations. arXiv preprint arXiv:1804.00064.
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