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The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to understand their potential biases.
Classification accuracy score for conditional generative models
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Model cards for model reporting
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Assessing Generative Models via Precision and Recall
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Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Improved precision and recall metric for assessing generative models, 2019
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Multimodal datasets: misogyny, pornography, and malignant stereotypes
Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe · 2021
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Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach · 2021
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Instance-conditioned gan
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Learning transferable visual models from natural language supervision
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High-resolution image synthesis with latent diffusion models
The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world
William A Gaviria Rojas, Sudnya Diamos, Keertan Ranjan Kini, David Kanter, Vijay Janapa Reddi, and Cody Coleman · 2022
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Photorealistic text-to-image diffusion models with deep language understanding, 2022
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Revisiting weakly supervised pre-training of visual perception models
Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross B. Girshick, Piotr Dollár, and Laurens van der Maaten · 2022
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"i’m sorry to hear that": Finding new biases in language models with a holistic descriptor dataset, 2022
Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, and Adina Williams · 2022
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Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
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Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Rebecca Pantofaru · 2021
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Image representations learned with unsupervised pre-training contain human-like biases
Ryan Steed and Aylin Caliskan · 2021
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Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang · 2022
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Easily accessible text-to-image generation amplifies demographic stereotypes at large scale, 2022
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Robert Wolfe and Aylin Caliskan · 2022
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American == white in multimodal language-and-image ai
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Inspecting the geographical representativeness of images from text-to-image models
Abhipsa Basu, R Venkatesh Babu, and Danish Pruthi · 2023
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The casual conversations v2 dataset
Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, and Caner Hazirbas · 2023
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models, 2023
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