Acknowledgement
Dr. Alharbi would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (23UQU43101400DSR005). She also would like to express her gratitude for support this research ID:4401095348.
References
- D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning representations by back- propagating errors, Nature 323 (1986) 533-536. https://doi.org/10.1038/323533a0
- H. L. Jungang Xu and S. Zhou, An overview of deep generative models, IETE Tech- nical Review 32(2) (2015) 131-139. https://doi.org/10.1080/02564602.2014.987328
- A. Razavi, A. Van den Oord and O. Vinyals, Generating diverse high-fidelity images with vq-vae-2, Advances in neural information processing systems 32 (2019).
- T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., Language models are few-shot learners, Advances in neural information processing systems 33 (2020) 1877-1901.
- P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford and I. Sutskever, Jukebox: A generative model for music, arXiv preprint arXiv:2005.00341 (2020).
- M. Kumar, M. Babaeizadeh, D. Erhan, C. Finn, S. Levine, L. Dinh and D. Kingma, Videoflow: A flow-based generative model for video, arXiv preprint arXiv:1903.01434 2(5) (2019) p. 3.
- T. Marwah, G. Mittal and V. N. Balasubramanian, Attentive semantic video genera-tion using captions, in Proceedings of the IEEE international conference on computer vision2017, pp. 1426-1434.
- E. I. Nikolaev, Opportunities and challenges in deep generative models, in CEUR Workshop Proceedings2018, pp. 326-329.
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, Generative adversarial networks, Communications of the ACM 63(11) (2020) 139-144. https://doi.org/10.1145/3422622
- M. Elasri, O. Elharrouss, S. Al-ma'adeed and H. Tairi, Image generation: A review, Neural Processing Letters 54 (03 2022).
- J. Ho, A. Jain and P. Abbeel, Denoising diffusion probabilistic models, Advances in neural information processing systems 33 (2020) 6840-6851.
- P. Dhariwal and A. Nichol, Diffusion models beat gans on image synthesis, Advances in neural information processing systems 34 (2021) 8780-8794.
- R. Rombach, A. Blattmann, D. Lorenz, P. Esser and B. Ommer, High-resolution image synthesis with latent diffusion models, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)June 2022, pp. 10684-10695.