Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models
<|reference_start|>Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models: Advances in generative models have led to significant interest in image synthesis, demonstrating the ability to generate high-quality images for a diverse range of text prompts. Despite this progress, most studies ignore the presence of bias. In this paper, we examine several text-to-image models not only by qualitatively assessing their performance in generating accurate images of human faces, groups, and specified numbers of objects but also by presenting a social bias analysis. As expected, models with larger capacity generate higher-quality images. However, we also document the inherent gender or social biases these models possess, offering a more complete understanding of their impact and limitations.<|reference_end|>
@article{masrourisaadat2024analyzing,
    title={Analyzing Quality, Bias, and Performance in Text-to-Image Generative
  Models},
    author={Nila Masrourisaadat, Nazanin Sedaghatkish, Fatemeh Sarshartehrani and
  Edward A. Fox},
    journal={arXiv preprint arXiv:2407.00138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00138},
    primaryClass={cs.AI cs.CV}
}