[19] Ben Hutchinson and Margaret Mitchell. 50 Years of Test (Un)fairness: Lessons for Machine Learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19, pages 49-58, New York, NY, USA, January 2019. Association for Computing Machinery. ISBN 978-1-4503-6125-5. doi: 10.1145/3287560.3287600.
[20] Christopher T. H. Teo and Ngai-Man Cheung. Measuring Fairness in Generative Models. 38th International Conference on Machine Learning (ICML) Workshop, July 2021.
[21] Harsh Jaykumar Jalan, Gautam Maurya, Canute Corda, Sunny Dsouza, and Dakshata Panchal. Suspect Face Generation. In 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA), pages 73-78, April 2020. doi: 10.1109/CSCITA47329.2020.9137812.
[22] Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan. Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing, 321:321-331, 2018.
[23] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
[24] Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional image synthesis with auxiliary classifier gans. In ICML, 2017.
[25] Kiran K Thekumparampil, Ashish Khetan, Zinan Lin, and Sewoong Oh. Robustness of conditional GANs to noisy labels. In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
[26] Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale, November 2022.
[27] Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image manipulation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[28] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022.
[29] Vivian Liu and Lydia B Chilton. Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1-23, 2022.
[30] Stable Diffusion: Prompt Guide and Examples. https://strikingloo.github.io/stable-diffusion-vs-dalle-2, August 2022.
[31] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to Prompt for Vision-Language Models. International Journal of Computer Vision, 130(9):2337-2348, September 2022. ISSN 0920-5691, 1573-1405. doi: 10.1007/s11263-022-01653-1.
[32] Martin Haspelmath. Indefinite Pronouns. Oxford University Press, 1997. ISBN 978-0-19-829963-9 978-0-19-823560-6. doi: 10.1093/oso/9780198235606.001.0001.
[33] Abigail C Saguy and Juliet A Williams. A little word that means a lot: A reassessment of singular they in a new era of gender politics. Gender & Society, 36(1):5-31, 2022.
[34] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, June 2016.
[35] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4510-4520, Salt Lake City, UT, June 2018. IEEE. ISBN 978-1-5386-6420-9. doi: 10.1109/CVPR.2018.00474.
[36] Karen Simonyan and Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. ICLR2015, September 2014.
[37] C Radhakrishna Rao. Maximum likelihood estimation for the multinomial distribution. Sankhya: The Indian Journal of Statistics (1933-1960), 18(1/2):139-148, 1957.