[15] Krizhevsky, Alex, Hinton, Geoffrey, et al. Learning multiple layers of features from tiny images. 2009. 3
[16] Lee, Kimin, Smith, Laura, and Abbeel, Pieter. Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training. arXiv preprint arXiv:2106.05091, 2021. 3
[17] Lee, Kimin, Liu, Hao, Ryu, Moonkyung, Watkins, Olivia, Du, Yuqing, Boutilier, Craig, Abbeel, Pieter, Ghavamzadeh, Mohammad, and Gu, Shixiang Shane. Aligning text-to-image models using human feedback. arXiv preprint arXiv:2302.12192, 2023. 1, 2, 3, 6, 9, 18
[18] Li, Junnan, Li, Dongxu, Xiong, Caiming, and Hoi, Steven. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, 2022. 3, 7, 18
[19] Liu, Hao, Sferrazza, Carmelo, and Abbeel, Pieter. Chain of hindsight aligns language models with feedback. arXiv preprint arXiv:2302.02676, 2023. 3
[20] Liu, Nan, Li, Shuang, Du, Yilun, Torralba, Antonio, and Tenenbaum, Joshua B. Compositional visual generation with composable diffusion models. In European Conference on Computer Vision, 2022. 2
[21] Liu, Rosanne, Garrette, Dan, Sahara, Chitwan, Chan, William, Roberts, Adam, Narang, Sharan, Blok, Irina, Mical, RJ, Norouzi, Mohammad, and Constant, Noah. Character-aware models improve visual text rendering. arXiv preprint arXiv:2212.10562, 2022. 2
[22] Liu, Ziwei, Luo, Ping, Wang, Xiaogang, and Tang, Xiaou. Deep learning face attributes in the wild. In International Conference on Computer Vision, 2015. 3
[23] Ouyang, Long, Wu, Jeffrey, Jiang, Xu, Almeida, Diogo, Wainwright, Carroll, Mishkin, Pamela, Zhang, Chong, Agarwal, Sandhini, Slama, Katarina, Ray, Alex, et al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, 2022. 3, 5
[24] Peters, Jan and Schaal, Stefan. Reinforcement learning by reward-weighted regression for operational space control. In Proceedings of the 24th international conference on Machine learning, pp. 745-750, 2007. 6
[25] Petsiuk, Vitali, Siemenn, Alexander E, Surbehera, Saisamrit, Chin, Zad, Tyser, Keith, Hunter, Gregory, Raghavan, Arvind, Hicke, Yann, Plummer, Bryan A, Kerret, Ori, et al. Human evaluation of text-to-image models on a multi-task benchmark. arXiv preprint arXiv:2211.12112, 2022. 1
[26] Poole, Ben, Jain, Ajay, Barron, Jonathan T, and Mildenhall, Ben. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 2
[27] Radford, Alec, Kim, Jong Wook, Hallacy, Chris, Ramesh, Aditya, Goh, Gabriel, Agarwal, Sandhini, Sastry, Girish, Askell, Amanda, Mishkin, Pamela, Clark, Jack, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021. 1, 2, 3, 7, 18
[28] Raffel, Colin, Shazeer, Noam, Roberts, Adam, Lee, Katherine, Narang, Sharan, Matena, Michael, Zhou, Yanqi, Li, Wei, and Liu, Peter J. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485-5551, 2020. 1, 2
[29] Ramesh, Aditya, Dhariwal, Prafulla, Nichol, Alex, Chu, Casey, and Chen, Mark. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. 1, 2
[30] Rombach, Robin, Blattmann, Andreas, Lorenz, Dominik, Esser, Patrick, and Ommer, Björn. High-resolution image synthesis with latent diffusion models. In Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 3, 5, 7