Introduction to Generative Adversarial Networks Challenges and Solutions

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2021
https://doi.org/10.47164/ijngc.v12i5.468
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 12, Special Issue 5, November 2021

##plugins.themes.academic_pro.article.main##

Harmeet Khanuja
a:1:{s:5:"en_US";s:19:"Associate Professor";}
AARTI AMOD AGARKAR

Abstract


Deep learning has received spectacular adoption in the field of artificial intelligence. With this many deep learning models have been developed. Generative Adversarial Networks (GAN) is one of the deep learning model, which is based on the two-player game from the Game theory. Two neural networks namely; generator and discriminator compete with each other. The proposition of the model variation is to achieve the data distribution through unsupervised learning to generate more realistic data. At present, GANs have been widely studied due to the extensive application prospects which include computer vision like generating lot of data, image to image translation etc. In this paper, the background of the GAN with theoretic model is introduced. Finally the existing research challenges of GANs are discussed with probable solutions giving a wider area of research.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Khanuja, H., & AGARKAR, A. A. . (2021). Introduction to Generative Adversarial Networks Challenges and Solutions. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.468
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. ARJOVSKY, M., CHINTALA, S. AND BOTTOU, L., 2017, July. Wasserstein generative adversarial networks. In International conference on machine learning (pp. 214-223). PMLR.
  2. ARJOVSKY, M. AND BOTTOU, L., 2017. Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862.
  3. BHAVSAR, H. AND PANCHAL, M.H., 2012. A review on support vector machine for data classification. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), pp.185-189.
  4. CAI, J., MENG, Z., KHAN, A.S., O’REILLY, J., LI, Z., HAN, S. AND TONG, Y., 2021, September. Identity-free facial expression recognition using conditional generative adversarial network. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 1344-1348). IEEE. DOI: https://doi.org/10.1109/ICIP42928.2021.9506593
  5. COVER, T. AND HART, P., 1967. Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), pp.21-27. DOI: https://doi.org/10.1109/TIT.1967.1053964
  6. GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., XU, B., WARDE-FARLEY, D., OZAIR, S., COURVILLE, A. AND BENGIO, Y., 2020. Generative adversarial networks. Communications of the ACM, 63(11), pp.139-144. DOI: https://doi.org/10.1145/3422622
  7. GOODFELLOW, I., 2016. Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160.
  8. GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., XU, B., WARDE-FARLEY, D., OZAIR, S., COURVILLE, A. AND BENGIO, Y., 2014. Generative adversarial nets. Advances in neural information processing systems, 27.
  9. JASON BROWNLEE, Impressive Applications of Generative Adversarial Networks (GANs), https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/, 2019
  10. KLADIS, E., AKASIADIS, C., MICHELIOUDAKIS, E., ALEVIZOS, E. AND ARTIKIS, A., 2021. An Empirical Evaluation of Early Time-Series Classification Algorithms. In EDBT/ICDT Workshops.
  11. KOLLER, D. AND FRIEDMAN, N., 2009. Probabilistic graphical models: principles and techniques. MIT press.
  12. MAKHZANI, A., SHLENS, J., JAITLY, N., GOODFELLOW, I. AND FREY, B., 2015. Adversarial autoencoders. arXiv preprint arXiv:1511.05644.
  13. NOJAVANASGHARI, B., HUANG, Y. AND KHAN, S., 2018. Interactive generative adversarial networks for facial expression generation in dyadic interactions. arXiv preprint arXiv:1801.09092.
  14. ROTH, K., LUCCHI, A., NOWOZIN, S. AND HOFMANN, T., 2017. Stabilizing training of generative adversarial networks through regularization. arXiv preprint arXiv:1705.09367.
  15. SALIMANS, T., GOODFELLOW, I., ZAREMBA, W., CHEUNG, V., RADFORD, A. AND CHEN, X., 2016. Improved techniques for training GANs. Advances in neural information processing systems, 29, pp.2234-2242.
  16. WANG, X., WANG, X. AND NI, Y., 2018. Unsupervised domain adaptation for facial expression recognition using generative adversarial networks. Computational intelligence and neuroscience, 2018. DOI: https://doi.org/10.1155/2018/7208794
  17. WALLACH, H.M., 2004. Conditional random fields: An introduction. Technical Reports (CIS), p.22.
  18. WENG, L., 2019. From GAN to WGAN. arXiv preprint arXiv:1904.08994.
  19. WRIGHT, R.E., 1995. Logistic regression.
  20. YEGNANARAYANA, B., 2009. Artificial neural networks. PHI Learning Pvt. Ltd..
  21. ZHAO, J., MATHIEU, M. AND LECUN, Y., 2016. Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126.
  22. GHOSH, A., KULHARIA, V., NAMBOODIRI, V.P., TORR, P.H. AND DOKANIA, P.K., 2018. Multi-agent diverse generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8513-8521). DOI: https://doi.org/10.1109/CVPR.2018.00888
  23. METZ, L., POOLE, B., PFAU, D. AND SOHL-DICKSTEIN, J., 2016. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163.