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All Journal International Journal of Applied Mathematics and Computing.
Hassan Gul
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Computer Science & IT Mathematics

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Understanding And Enhancing Diversity In Generative Models Ahmad, Munir; Chohan, Muhammad Kamran; Qureshi, Muhammad Zarif; Hassan Gul
International Journal of Applied Mathematics and Computing Vol. 1 No. 2 (2024): April : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i2.16

Abstract

This research delves into the crucial aspect of diversity within generative models, exploring both its understanding and potential enhancement. Diversity in generative models refers to the ability of the model to produce a wide range of outputs that cover the variability present in the underlying data distribution. Understanding diversity is fundamental for assessing the quality and applicability of generative models across various domains, including natural language processing, computer vision, and creative arts. We discusses existing methods and metrics for evaluating diversity in generative models and highlights the importance of diversity in promoting fairness, robustness, and creativity. It explores strategies for enhancing diversity in generative models, such as regularization techniques, diversity-promoting objectives, and novel architectures. By advancing our understanding of diversity and implementing techniques to enhance it, generative models can better capture the complexity and richness of real-world data, leading to improved performance and broader applicability.
Co-Authors Ahmad, Munir Chohan, Muhammad Kamran Qureshi, Muhammad Zarif