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Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer (2019) https://doi.org/10.48550/arXiv.1910.10683 [cs.LG]
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
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Badash, I., Burtt, K., Solorzano, C.A., Carey, J.N.: Innovations in surgery simulation: a review of past, current and future techniques. Annals of Translational Medicine 4
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021) https://doi.org/10.48550/arXiv.2103.00020 [cs.CV]
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Kim, S.W., Philion, J., Torralba, A., Fidler, S.: Drivegan: Towards a controllable high-quality neural simulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021). https://doi.org/10.48550/arXiv.2104.15060 . Oral. https://doi.org/10.48550/arXiv.2104.15060
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Peebles, W., Xie, S.: Scalable diffusion models with transformers. arXiv preprint 2212.09748
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Kaleta, J., Dall’Alba, D., Płotka, S., Korzeniowski, P.: Minimal data requirement for realistic endoscopic image generation with stable diffusion. International Journal of Computer Assisted Radiology and Surgery 19
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