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Diffusion models have recently shown remarkable success in high-quality image generation.
Rank analysis of incomplete block designs: I. the method of paired comparisons
R. A. Bradley and M. E. Terry · 1952
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Reverse-time diffusion equation models
B. D. Anderson · 1982
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Estimating the error rate of a prediction rule: Improvement on cross-validation
B. Efron · 1983
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Improvements on cross-validation: The 632+ bootstrap method
B. Efron and R. Tibshirani · 1997
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Pattern Classification
R. O. Duda, P. E. Hart, and D. G. Stork · 2006
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F.-F. Li · 2009
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
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The mnist database of handwritten digit images for machine learning research
L. Deng · 2012
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Generative visual manipulation on the natural image manifold
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros · 2016
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Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
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Improving image generative models with human interactions
A. K. Lampinen, D. So, D. Eck, and F. Bertsch · 2017
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
J. Buckman, D. Hafner, G. Tucker, E. Brevdo, and H. Lee · 2018
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Model-ensemble trust-region policy optimization
T. Kurutach, I. Clavera, Y. Duan, A. Tamar, and P. Abbeel · 2018
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
Cited alongside, same era.
Applied Stochastic Differential Equations
S. Särkkä and A. Solin · 2019
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Fine-tuning language models from human preferences
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Learning to summarize with human feedback
N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano · 2020
Cited alongside, same era.
Large image datasets: A pyrrhic win for computer vision?
A. Birhane and V. U. Prabhu · 2021
Cited alongside, same era.
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe · 2022
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Hierarchical text-conditional image generation with CLIP latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi · 2022
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Interactive optimization of generative image modelling using sequential subspace search and content-based guidance
T. Chong, I.-C. Shen, I. Sato, and T. Igarashi · 2021
Cited alongside, same era.
Diffusion models beat GANs on image synthesis
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
J. Ho and T. Salimans · 2021
Cited alongside, same era.
PEBBLE: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training
K. Lee, L. Smith, and P. Abbeel · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
Cited alongside, same era.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2021
Cited alongside, same era.
A. Bansal, H.-M. Chu, A. Schwarzschild, S. Sengupta, M. Goldblum, J. Geiping, and T. Goldstein · 2023
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Extracting training data from diffusion models
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace · 2023
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Erasing concepts from diffusion models
R. Gandikota, J. Materzynska, J. Fiotto-Kaufman, and D. Bau · 2023
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Enhancing diffusion-based image synthesis with robust classifier guidance
B. Kawar, R. Ganz, and M. Elad · 2023
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Refining generative process with discriminator guidance in score-based diffusion models
D. Kim, Y. Kim, S. J. Kwon, W. Kang, and I.-C. Moon · 2023
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Aligning text-to-image models using human feedback
K. Lee, H. Liu, M. Ryu, O. Watkins, Y. Du, C. Boutilier, P. Abbeel, M. Ghavamzadeh, and S. S. Gu · 2023
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Training stable diffusion from scratch costs <160k
MosaicML · 2023
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( @EMostaque
E. Mostaque · 2023
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Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization
R. Ramamurthy, P. Ammanabrolu, K. Brantley, J. Hessel, R. Sifa, C. Bauckhage, H. Hajishirzi, and Y. Choi · 2023
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Zeroth-order optimization meets human feedback: Provable learning via ranking oracles
Z. Tang, D. Rybin, and T.-H. Chang · 2023
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Don’t play favorites: Minority guidance for diffusion models
S. Um and J. C. Ye · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
E. Zhang, K. Wang, X. Xu, Z. Wang, and H. Shi · 2023
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