Fetching the paper…
Reading the bibliography…
We present a comprehensive solution to learn and improve text-to-image models from human preference feedback.
Visualizing data using t-sne
L. van der Maaten and G. Hinton · 2008
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
An actor-critic algorithm for sequence prediction
D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, and Y. Bengio · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences, Jun 2017
P. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Interactive learning from policy-dependent human feedback, Aug 2017
J. MacGlashan, M. Ho, R. Loftin, B. Peng, G. Wang, D. Roberts, M. Taylor, and M. Littman · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Earlier work this paper cites.
S. Barratt and R. Sharma · 2018
Earlier work this paper cites.
Reward learning from human preferences and demonstrations in atari, Nov 2018
B. Ibarz, J. Leike, T. Pohlen, G. Irving, S. Legg, and D. Amodei · 2018
Earlier work this paper cites.
Can neural machine translation be improved with user feedback?
J. Kreutzer, S. Khadivi, E. Matusov, and S. Riezler · 2018
Earlier work this paper cites.
Deep tamer: Interactive agent shaping in high-dimensional state spaces
G. Warnell, N. Waytowich, V. Lawhern, and P. Stone · 2018
Earlier work this paper cites.
Better rewards yield better summaries: Learning to summarise without references
F. Böhm, Y. Gao, C. M. Meyer, O. Shapira, I. Dagan, and I. Gurevych · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
N. Reimers and I. Gurevych · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences., Sep 2019
D. Ziegler, N. Stiennon, J. Wu, T. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving · 2019
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Earlier work this paper cites.
Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Earlier work this paper cites.
Mpnet: Masked and permuted pre-training for language understanding
K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning to compare for better training and evaluation of open domain natural language generation models
W. Zhou and K. Xu · 2020
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis, Dec 2021
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
Cogview: Mastering text-to-image generation via transformers
M. Ding, Z. Yang, W. Hong, W. Zheng, C. Zhou, D. Yin, J. Lin, X. Zou, Z. Shao, H. Yang, et al · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
P. Esser, R. Rombach, and B. Ommer · 2021
Cited alongside, same era.
Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training
K. Lee, L. M. Smith, and P. Abbeel · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
Later among the works it cites.
Human evaluation of text-to-image models on a multi-task benchmark
V. Petsiuk, A. E. Siemenn, S. Surbehera, Z. Chin, K. Tyser, G. Hunter, A. Raghavan, Y. Hicke, B. A. Plummer, O. Kerret, et al · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. L. Li and P. Liang · 2021
Cited alongside, same era.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
X. Liu, K. Ji, Y. Fu, Z. Du, Z. Yang, and J. Tang · 2021
Cited alongside, same era.
Self-supervised learning: Generative or contrastive
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang · 2021
Cited alongside, same era.
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang · 2021
Cited alongside, same era.
Webgpt: Browser-assisted question-answering with human feedback
R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, et al · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever · 2021
Cited alongside, same era.
Later among the works it cites.
Image super-resolution via iterative refinement
C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, M. Gallé, et al · 2022
Later among the works it cites.
Training language models with natural language feedback, Apr 2022
J. Scheurer, J. Campos, J. Chan, A. Chen, K. Cho, and E. Perez · 2022
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
C. Schuhmann, R. Beaumont, R. Vencu, C. W. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al · 2022
Later among the works it cites.
Selective annotation makes language models better few-shot learners
H. Su, J. Kasai, C. H. Wu, W. Shi, T. Wang, J. Xin, R. Zhang, M. Ostendorf, L. Zettlemoyer, N. A. Smith, et al · 2022
Later among the works it cites.
DiffusionDB: A large-scale prompt gallery dataset for text-to-image generative models
Z. J. Wang, E. Montoya, D. Munechika, H. Yang, B. Hoover, and D. H. Chau · 2022
Later among the works it cites.
Versatile diffusion: Text, images and variations all in one diffusion model
X. Xu, Z. Wang, E. Zhang, K. Wang, and H. Shi · 2022
Later among the works it cites.
Scaling autoregressive models for content-rich text-to-image generation
J. Yu, Y. Xu, J. Y. Koh, T. Luong, G. Baid, Z. Wang, V. Vasudevan, A. Ku, Y. Yang, B. K. Ayan, et al · 2022
Later among the works it cites.
Glm-130b: An open bilingual pre-trained model
A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, et al · 2022
Later among the works it cites.
Opt: Open pre-trained transformer language models
S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, et al · 2022
Later among the works it cites.
Raft: Reward ranked finetuning for generative foundation model alignment
H. Dong, W. Xiong, D. Goyal, R. Pan, S. Diao, J. Zhang, K. Shum, and T. Zhang · 2023
Closest in time.
Pick-a-pic: An open dataset of user preferences for text-to-image generation
Y. Kirstain, A. Polyak, U. Singer, S. Matiana, J. Penna, and O. Levy · 2023
Closest in time.
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
Closest in time.
Chain of hindsight aligns language models with feedback, Feb 2023
H. Liu, C. Sferrazza, and P. Abbeel · 2023
Closest in time.
Toward verifiable and reproducible human evaluation for text-to-image generation
M. Otani, R. Togashi, Y. Sawai, R. Ishigami, Y. Nakashima, E. Rahtu, J. Heikkilä, and S. Satoh · 2023
Closest in time.
Better aligning text-to-image models with human preference
X. Wu, K. Sun, F. Zhu, R. Zhao, and H. Li · 2023
Closest in time.
User behavior simulation for search result re-ranking
J. Zhang, Y. Liu, J. Mao, W. Ma, J. Xu, S. Ma, and Q. Tian · 2023
Closest in time.