[36] W. Peebles and S. Xie. Scalable diffusion models with transformers. arXiv preprint arXiv:2212.09748, 2022.
[37] M. Prabhudesai, A. Goyal, S. Paul, S. Van Steenkiste, M. S. Sajjadi, G. Aggarwal, T. Kipf, D. Pathak, and K. Fragkiadaki. Test-time adaptation with slot-centric models. In International Conference on Machine Learning, pages 28151-28166. PMLR, 2023.
[38] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[39] B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. DoImagenet classifiers generalize toImagenet? In International conference on machine learning, pages 5389-5400. PMLR, 2019.
[40] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022.
[41] C. Sahara, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35: 36479-36494, 2022.
[42] M. Singh, Q. Duval, K. V. Alwala, H. Fan, V. Aggarwal, A. Adcock, A. Joulin, P. Dollár, C. Feichtenhofer, R. Girshick, et al. The effectiveness of mae pre-pretraining for billion-scale pretraining. arXiv preprint arXiv:2303.13496, 2023.
[43] Y. Song, P. Dhariwal, M. Chen, and I. Sutskever. Consistency models. 2023.
[44] Y. Sun, X. Wang, Z. Liu, J. Miller, A. Efros, and M. Hardt. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning, pages 9229-9248. PMLR, 2020.
[45] B. Trabucco, K. Doherty, M. Gurinas, and R. Salakhutdinov. Effective data augmentation with diffusion models. arXiv preprint arXiv:2302.07944, 2023.
[46] D. Wang, E. Shelhamer, S. Liu, B. Olshausen, and T. Darrell. Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726, 2020.
[47] Q. Wang, O. Fink, L. Van Gool, and D. Dai. Continual test-time domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7201-7211, 2022.
[48] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004.
[49] E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021.
[50] J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang, and S. De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. arXiv preprint arXiv:2303.04803, 2023.
[51] T. Yu, T. Xiao, A. Stone, J. Tompson, A. Brohan, S. Wang, J. Singh, C. Tan, J. Peralta, B. Ichter, et al. Scaling robot learning with semantically imagined experience. arXiv preprint arXiv:2302.11550, 2023.
[52] A. Yuille and D. Kersten. Vision as bayesian inference: analysis by synthesis? Trends in cognitive sciences, 10:301-8, 08 2006. doi: 10.1016/j.tics.2006.05.002.
[53] A. Yuille and D. Kersten. Vision as Bayesian inference: analysis by synthesis? Trends in Cognitive Sciences, 10:301-308, 2006.
[54] L. Zhang and M. Agrawala. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023.
[55] H. Zhao, Y. Liu, A. Alahi, and T. Lin. On pitfalls of test-time adaptation. arXiv preprint arXiv:2306.03536, 2023.