Abstract
In recent years there is a spike in text-to-image synthesis research. Most of the researches use Generative Adversarial Network (GAN) because of its effectiveness in generating a realistic synthetic image. In this paper, we provide several recent papers that focus on GAN based text-to-image synthesis and discuss their architecture, advantages of the model, dataset, and evaluation metric.
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Acknowledgement
This research is fully supported by Universiti Sains Malaysia Research University Individual (RUI) Research Grant 1001/PELECT/8014056.
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Che Aminudin, M.F., Suandi, S.A. (2022). Review on Generative Adversarial Neural Networks (GAN) in Text-to-Image Synthesis. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_134
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DOI: https://doi.org/10.1007/978-981-16-8129-5_134
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