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Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract)

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KI 2024: Advances in Artificial Intelligence (KI 2024)

Abstract

Deep generative models have shown outstanding sample quality in a wide variety of modalities.

D. Molchanov and V. Ohanesian—Independent Researcher

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References

  1. Bansal, A., et al.: Cold diffusion: inverting arbitrary image transforms without noise. arXiv preprint arXiv:2208.09392 (2022)

  2. Chen, R.T., Behrmann, J., Duvenaud, D.K., Jacobsen, J.H.: Residual flows for invertible generative modeling. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  3. EOSDIS: land, atmosphere near real-time capability for eos (lance) system operated by nasas earth science data and information system (esdis) (2020). https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data

  4. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)

    Google Scholar 

  5. Grathwohl, W., Chen, R.T., Bettencourt, J., Sutskever, I., Duvenaud, D.: Ffjord: free-form continuous dynamics for scalable reversible generative models. arXiv preprint arXiv:1810.01367 (2018)

  6. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  7. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239 (2020)

  8. Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., Welling, M.: Argmax flows and multinomial diffusion: learning categorical distributions. Adv. Neural. Inf. Process. Syst. 34, 12454–12465 (2021)

    Google Scholar 

  9. Karras, T., et al.: Alias-free generative adversarial networks. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  10. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

  11. Mahoney, M.: Large text compression benchmark (2011). http://www.mattmahoney.net/dc/text.html

  12. Nachmani, E., Roman, R.S., Wolf, L.: Denoising diffusion gamma models. arXiv preprint arXiv:2110.05948 (2021)

  13. Nichol, A., Dhariwal, P.: Improved denoising diffusion probabilistic models. arXiv preprint arXiv:2102.09672 (2021)

  14. Ramesh, A., et al.: Zero-shot text-to-image generation. In: International Conference on Machine Learning, pp. 8821–8831. PMLR (2021)

    Google Scholar 

  15. Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning, pp. 1278–1286. PMLR (2014)

    Google Scholar 

  16. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256–2265. PMLR (2015)

    Google Scholar 

  17. Xiao, Z., Kreis, K., Kautz, J., Vahdat, A.: Vaebm: a symbiosis between variational autoencoders and energy-based models. arXiv preprint arXiv:2010.00654 (2020)

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Correspondence to Dmitry Vetrov .

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Okhotin, A. et al. (2024). Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract). In: Hotho, A., Rudolph, S. (eds) KI 2024: Advances in Artificial Intelligence. KI 2024. Lecture Notes in Computer Science(), vol 14992 . Springer, Cham. https://doi.org/10.1007/978-3-031-70893-0_31

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  • DOI: https://doi.org/10.1007/978-3-031-70893-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70892-3

  • Online ISBN: 978-3-031-70893-0

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