1. Angelopoulos, A.N., et al.: Image-to-image regression with distribution-free uncertainty quantification and applications in imaging. In: International Conference on Machine Learning, pp. 717–730. PMLR (2022)
2. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)
3. Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., Ozcan, A.: Deep learning-enabled virtual histological staining of biological samples (2023). https://doi.org/10.1038/s41377-023-01104-7. https://www.nature.com/articles/s41377-023-01104-7
4. Bates, S., Angelopoulos, A., Lei, L., Malik, J., Jordan, M.: Distribution-free, risk-controlling prediction sets. J. ACM (JACM) 68(6), 1–34 (2021)
5. Blau, Y., Michaeli, T.: The perception-distortion tradeoff. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, June 2018. https://doi.org/10.1109/cvpr.2018.00652