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Diffusion models, known for their tremendous ability to generate novel and high-quality samples, have recently raised concerns due to their data memorization behavior, which poses privacy risks.
Dwork, C.: Differential privacy. In: International colloquium on automata, languages, and programming. pp. 1–12. Springer (2006)
2006
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. pp. 308–318 (2016)
2016
Earlier work this paper cites.
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. pp. 1273–1282. PMLR (2017)
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Earlier work this paper cites.
Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: Stargan v2: Diverse image synthesis for multiple domains. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8188–8197 (2020)
2020
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM 63
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al.: Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 2633–2650 (2021)
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Midjourney team (2022), https://www.midjourney.com/home
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Pizzi, E., Roy, S.D., Ravindra, S.N., Goyal, P., Douze, M.: A self-supervised descriptor for image copy detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14532–14542 (2022)
Ni, Z., Wei, L., Li, J., Tang, S., Zhuang, Y., Tian, Q.: Degeneration-tuning: Using scrambled grid shield unwanted concepts from stable diffusion. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 8900–8909 (2023)
2023
Later among the works it cites.
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Diffusion art or digital forgery? investigating data replication in diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6048–6058 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wen, Y., Liu, Y., Chen, C., Lyu, L.: Detecting, explaining, and mitigating memorization in diffusion models. In: The Twelfth International Conference on Learning Representations (2023)
2023
Later among the works it cites.
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2022
Cited alongside, same era.
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
2022
Cited alongside, same era.
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., Wallace, E.: Extracting training data from diffusion models. In: 32nd USENIX Security Symposium (USENIX Security 23). pp. 5253–5270 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Kumari, N., Zhang, B., Wang, S.Y., Shechtman, E., Zhang, R., Zhu, J.Y.: Ablating concepts in text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 22691–22702 (2023)
2023
Cited alongside, same era.
Yoon, T., Choi, J.Y., Kwon, S., Ryu, E.K.: Diffusion probabilistic models generalize when they fail to memorize. In: ICML 2023 Workshop on Structured Probabilistic Inference { \{ \ \backslash & } \} Generative Modeling (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Sora team (2024), https://openai.com/sora
2024
Closest in time.
Daras, G., Shah, K., Dagan, Y., Gollakota, A., Dimakis, A., Klivans, A.: Ambient diffusion: Learning clean distributions from corrupted data. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Gandikota, R., Orgad, H., Belinkov, Y., Materzyńska, J., Bau, D.: Unified concept editing in diffusion models. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 5111–5120 (2024)
2024
Closest in time.
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Understanding and mitigating copying in diffusion models. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Wen, Y., Jain, N., Kirchenbauer, J., Goldblum, M., Geiping, J., Goldstein, T.: Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery. Advances in Neural Information Processing Systems 36
2024
Closest in time.