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Diffusion models are becoming increasingly popular in synthetic data generation and image editing applications.
Face recognition performance: Role of demographic information
B. F. Klare, M. J. Burge, J. C. Klontz, R. W. Vorder Bruegge, and A. K. Jain · 2012
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
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Characterizing the variability in face recognition accuracy relative to race
K. K. S, K. Vangara, M. C. King, V. Albiero, and K. Bowyer · 2019
Earlier work this paper cites.
How does gender balance in training data affect face recognition accuracy?
V. Albiero, K. Zhang, and K. W. Bowyer · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Earlier work this paper cites.
Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila · 2020
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Mitigating bias in face recognition using skewness-aware reinforcement learning
M. Wang and W. Deng · 2020
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Inclusive gan: Improving data and minority coverage in generative models
N. Yu, K. Li, P. Zhou, J. Malik, L. Davis, and M. Fritz · 2020
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Gendered differences in face recognition accuracy explained by hairstyles, makeup, and facial morphology
V. Albiero, K. Zhang, M. C. King, and K. Bowyer · 2021
Cited alongside, same era.
Pass: Protected attribute suppression system for mitigating bias in face recognition
P. Dhar, J. Gleason, A. Roy, C. D. Castillo, and R. Chellappa · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
When do gans replicate? on the choice of dataset size
Q. Feng, C. Guo, F. Benitez-Quiroz, and A. Martinez · 2021
Cited alongside, same era.
Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
K. Kärkkäinen and J. Joo · 2021
Cited alongside, same era.
Generative adversarial networks for image and video synthesis: Algorithms and applications
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2022
Later among the works it cites.
Studying bias in gans through the lens of race
V. H. Maluleke, N. Thakkar, T. Brooks, E. Weber, T. Darrell, A. A. Efros, A. Kanazawa, and D. Guillory · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
Later among the works it cites.
Image super-resolution via iterative refinement
C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi · 2022
Later among the works it cites.
Diffusion art or digital forgery? investigating data replication in diffusion models
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein · 2022
Later among the works it cites.
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M.-Y. Liu, X. Huang, J. Yu, T.-C. Wang, and A. Mallya · 2021
Cited alongside, same era.
Improving the fairness of deep generative models without retraining, 2021
S. Tan, Y. Shen, and B. Zhou · 2021
Cited alongside, same era.
Face regions impact recognition accuracy differently across demographics
V. Albiero, K. W. Bowyer, and M. C. King · 2022
Cited alongside, same era.
Perception prioritized training of diffusion models
J. Choi, J. Lee, C. Shin, S. Kim, H. Kim, and S. Yoon · 2022
Cited alongside, same era.
Cascaded diffusion models for high fidelity image generation
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans · 2022
Cited alongside, same era.
Imperfect imaganation: Implications of gans exacerbating biases on facial data augmentation and snapchat face lenses
N. Jain, A. Olmo, S. Sengupta, L. Manikonda, and S. Kambhampati · 2022
Cited alongside, same era.
Repfair-gan: Mitigating representation bias in gans using gradient clipping, 2022
P. J. Kenfack, K. Sabbagh, A. R. Rivera, and A. Khan · 2022
Cited alongside, same era.
S. Azizi, S. Kornblith, C. Saharia, M. Norouzi, and D. J. Fleet · 2023
Closest in time.
Synthetic data for face recognition: Current state and future prospects
F. Boutros, V. Struc, J. Fierrez, and N. Damer · 2023
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Extracting training data from diffusion models, 2023
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace · 2023
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Stable bias: Analyzing societal representations in diffusion models, 2023
A. S. Luccioni, C. Akiki, M. Mitchell, and Y. Jernite · 2023
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Social biases through the text-to-image generation lens, 2023
R. Naik and B. Nushi · 2023
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Getty images sues ai art generator stable diffusion in the us for copyright infringement
J. Vincent · 2023
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Face recognition accuracy across demographics: Shining a light into the problem
H. Wu, V. Albiero, K. S. Krishnapriya, M. C. King, and K. W. Bowyer · 2023
Closest in time.