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This work addresses the challenge of quantifying originality in text-to-image (T2I) generative diffusion models, with a focus on copyright originality.
471 u.s. 539
Harper & Row, Publishers, Inc. v. Nation Enterprises · 1985
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17 U.S.C. § 102(a)
U.S.C · 1990
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499 u.s. 340
Feist Publications · 1991
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Learners that use little information
Bassily, R., Moran, S., Nachum, I., Shafer, J., and Yehudayoff, A · 2018
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Bias and generalization in deep generative models: An empirical study
Zhao, S., Ren, H., Yuan, A., Song, J., Goodman, N., and Ermon, S · 2018
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How much does your data exploration overfit? controlling bias via information usage
Russo, D. and Zou, J · 2019
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OpenCLIP
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Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al · 2022
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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Chefer, H., Alaluf, Y., Vinker, Y., Wolf, L., and Cohen-Or, D · 2023
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Wang, L., Shen, G., Li, Y., and Chen, Y.-c
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Provable copyright protection for generative models
Vyas, N., Kakade, S., and Barak, B · 2023
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Attias, I., Dziugaite, G. K., Haghifam, M., Livni, R., and Roy, D. M · 2024
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