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Recent advances in powerful pre-trained diffusion models encourage the development of methods to improve the sampling performance under well-trained diffusion models.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Stochastic simulation
Ripley, B. D · 2009
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Variational rejection sampling
Grover, A., Gummadi, R., Lazaro-Gredilla, M., Schuurmans, D., and Ermon, S · 2018
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Discriminator rejection sampling
Azadi, S., Olsson, C., Darrell, T., Goodfellow, I., and Odena, A · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Metropolis-hastings generative adversarial networks
Turner, R., Hung, J., Frank, E., Saatchi, Y., and Yosinski, J · 2019
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Stargan v2: Diverse image synthesis for multiple domains
Choi, Y., Uh, Y., Yoo, J., and Ha, J.-W · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Telescoping density-ratio estimation
Rhodes, B., Xu, K., and Gutmann, M. U · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Clipscore: A reference-free evaluation metric for image captioning
Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., and Choi, Y · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
Cited alongside, same era.
Openclip, July 2021
Ilharco, G., Wortsman, M., Wightman, R., Gordon, C., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., and Schmidt, L · 2021
Cited alongside, same era.
Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
Cited alongside, same era.
Generating images with sparse representations
Nash, C., Menick, J., Dieleman, S., and Battaglia, P · 2021
Scalable adaptive computation for iterative generation
Jabri, A., Fleet, D. J., and Chen, T · 2023
Later among the works it cites.
Refining generative process with discriminator guidance in score-based diffusion models
Kim, D., Kim, Y., Kwon, S. J., Kang, W., and Moon, I.-C · 2023
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Understanding diffusion objectives as the ELBO with simple data augmentation
Kingma, D. P. and Gao, R · 2023
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Fp-diffusion: Improving score-based diffusion models by enforcing the underlying score fokker-planck equation
Lai, C.-H., Takida, Y., Murata, N., Uesaka, T., Mitsufuji, Y., and Ermon, S · 2023
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On distillation of guided diffusion models
Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., and Salimans, T · 2023
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A complete recipe for diffusion generative models
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Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
Cited alongside, same era.
Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A. Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., and Chen, M · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
Cited alongside, same era.
Pandey, K. and Mandt, S · 2023
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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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Consistency models
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2023
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Training unbiased diffusion models from biased dataset
Kim, Y., Na, B., Park, M., Jang, J., Kim, D., Kang, W., and chul Moon, I · 2024
Closest in time.
Label-noise robust diffusion models
Na, B., Kim, Y., Bae, H., Lee, J. H., Kwon, S. J., Kang, W., and chul Moon, I · 2024
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Elucidating the exposure bias in diffusion models
Ning, M., Li, M., Su, J., Salah, A. A., and Ertugrul, I. O · 2024
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Don’t play favorites: Minority guidance for diffusion models
Um, S., Lee, S., and Ye, J. C · 2024
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