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Score-based Generative Models (SGMs) is one leading method in generative modeling, renowned for their ability to generate high-quality samples from complex, high-dimensional data distributions.
Variable kernel density estimation
Terrell, G. R. and Scott, D. W · 1992
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2009
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Learning multiple layers of features from tiny images, 2009
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Analysis and geometry of Markov diffusion operators , volume 103
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Donahue, C., McAuley, J., and Puckette, M · 2018
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Introvae: Introspective variational autoencoders for photographic image synthesis
Huang, H., He, R., Sun, Z., Tan, T., et al · 2018
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High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., and Catanzaro, B · 2018
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Denoising diffusion probabilistic models, 2020
Ho, J., Jain, A., and Abbeel, P · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A · 2021
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Generative modeling with denoising auto-encoders and langevin sampling, 2022
Block, A., Mroueh, Y., and Rakhlin, A · 2022
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Chen, S., Chewi, S., Li, J., Li, Y., Salim, A., and Zhang, A. R · 2022
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Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V · 2022
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Diffuseq: Sequence to sequence text generation with diffusion models
Gong, S., Li, M., Feng, J., Wu, Z., and Kong, L · 2022
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Fastdiff: A fast conditional diffusion model for high-quality speech synthesis
Huang, R., Lam, M. W., Wang, J., Su, D., Yu, D., Ren, Y., and Zhao, Z · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Score-based generative modeling secretly minimizes the wasserstein distance
Kwon, D., Fan, Y., and Lee, K · 2022
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Extracting training data from diffusion models, 2023
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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Analysis of learning a flow-based generative model from limited sample complexity, 2023
Cui, H., Krzakala, F., Vanden-Eijnden, E., and Zdeborová, L · 2023
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On memorization in diffusion models
Gu, X., Du, C., Pang, T., Li, C., Lin, M., and Wang, Y · 2023
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Generalization in diffusion models arises from geometry-adaptive harmonic representation, 2023
Kadkhodaie, Z., Guth, F., Simoncelli, E. P., and Mallat, S · 2023
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Towards faster non-asymptotic convergence for diffusion-based generative models
Li, G., Wei, Y., Chen, Y., and Chi, Y · 2023
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Convergence for score-based generative modeling with polynomial complexity
Lee, H., Lu, J., and Tan, Y · 2022
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Diffusion-lm improves controllable text generation
Li, X., Thickstun, J., Gulrajani, I., Liang, P. S., and Hashimoto, T. B · 2022
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Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Diffusion art or digital forgery? investigating data replication in diffusion models, 2022
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., and Goldstein, T · 2022
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Convergence in kl divergence of the inexact langevin algorithm with application to score-based generative models
Wibisono, A. and Yingxi Yang, K · 2022
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Stochastic interpolants: A unifying framework for flows and diffusions
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E · 2023
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The New York Times sued OpenAI and Microsoft for copyright infringement, 2023
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Diffusion models are minimax optimal distribution estimators, 2023
Oko, K., Akiyama, S., and Suzuki, T · 2023
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Understanding and mitigating copying in diffusion models
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., and Goldstein, T · 2023
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Guaranteed optimal generative modeling with maximum deviation from the empirical distribution
Vardanyan, E., Minasyan, A., Hunanyan, S., Galstyan, T., and Dalalyan, A · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2023
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On the generalization of diffusion model, 2023
Yi, M., Sun, J., and Li, Z · 2023
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Diffusion probabilistic models generalize when they fail to memorize
Yoon, T., Choi, J. Y., Kwon, S., and Ryu, E. K · 2023
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