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这篇笔记承接上一篇技术文档的学习#xff0c;主要是为了做一个记录#xff0c;记录下openai sora技术介绍文档提到的一些论文#xff0c;再此特地记录一下#xff01; 1 原文…欢迎来到文思源想的ai空间这是技术老兵重学ai以及成长思考的第9篇分享
这篇笔记承接上一篇技术文档的学习主要是为了做一个记录记录下openai sora技术介绍文档提到的一些论文再此特地记录一下 1 原文引用文献汇总
Chiappa, Silvia, et al. Recurrent environment simulators. arXiv preprint arXiv:1704.02254 (2017).↩︎Ha, David, and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122 (2018).↩︎Vondrick, Carl, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. Advances in neural information processing systems 29 (2016).↩︎Tulyakov, Sergey, et al. Mocogan: Decomposing motion and content for video generation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.↩︎Clark, Aidan, Jeff Donahue, and Karen Simonyan. Adversarial video generation on complex datasets. arXiv preprint arXiv:1907.06571 (2019).↩︎Brooks, Tim, et al. Generating long videos of dynamic scenes. Advances in Neural Information Processing Systems 35 (2022): 31769-31781.↩︎Yan, Wilson, et al. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157 (2021).↩︎Wu, Chenfei, et al. Nüwa: Visual synthesis pre-training for neural visual world creation. European conference on computer vision. Cham: Springer Nature Switzerland, 2022.↩︎Ho, Jonathan, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 (2022).↩︎Blattmann, Andreas, et al. Align your latents: High-resolution video synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.↩︎Gupta, Agrim, et al. Photorealistic video generation with diffusion models. arXiv preprint arXiv:2312.06662 (2023).↩︎Vaswani, Ashish, et al. Attention is all you need. Advances in neural information processing systems 30 (2017).↩︎↩︎Brown, Tom, et al. Language models are few-shot learners. Advances in neural information processing systems 33 (2020): 1877-1901.↩︎↩︎Dosovitskiy, Alexey, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).↩︎↩︎Arnab, Anurag, et al. Vivit: A video vision transformer. Proceedings of the IEEE/CVF international conference on computer vision. 2021.↩︎↩︎He, Kaiming, et al. Masked autoencoders are scalable vision learners. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.↩︎↩︎Dehghani, Mostafa, et al. Patch nPack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. arXiv preprint arXiv:2307.06304 (2023).↩︎↩︎Rombach, Robin, et al. High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.↩︎Kingma, Diederik P., and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).↩︎Sohl-Dickstein, Jascha, et al. Deep unsupervised learning using nonequilibrium thermodynamics. International conference on machine learning. PMLR, 2015.↩︎Ho, Jonathan, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020): 6840-6851.↩︎Nichol, Alexander Quinn, and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. International Conference on Machine Learning. PMLR, 2021.↩︎Dhariwal, Prafulla, and Alexander Quinn Nichol. Diffusion Models Beat GANs on Image Synthesis. Advances in Neural Information Processing Systems. 2021.↩︎Karras, Tero, et al. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems 35 (2022): 26565-26577.↩︎Peebles, William, and Saining Xie. Scalable diffusion models with transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.↩︎Chen, Mark, et al. Generative pretraining from pixels. International conference on machine learning. PMLR, 2020.↩︎Ramesh, Aditya, et al. Zero-shot text-to-image generation. International Conference on Machine Learning. PMLR, 2021.↩︎Yu, Jiahui, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789 2.3 (2022): 5.↩︎Betker, James, et al. Improving image generation with better captions. Computer Science. https://cdn.openai.com/papers/dall-e-3. pdf 2.3 (2023): 8↩︎↩︎Ramesh, Aditya, et al. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1.2 (2022): 3.↩︎Meng, Chenlin, et al. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021).↩︎
2 原文引用文献翻译
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