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Recently, diffusion models were applied to a wide range of image analysis tasks.
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34
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Rasul, K., Seward, C., Schuster, I., Vollgraf, R.: Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In: International Conference on Machine Learning. pp. 8857–8868. PMLR (2021)
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Lan, L.C., Liu, T.J., Liu, K.H.: Age regression with specific facial landmarks by dual discriminator adversarial autoencoder. In: 2021 IEEE International Conference on Image Processing (ICIP). pp. 2718–2722. IEEE (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Liu, Z.S., Kalogeiton, V., Cani, M.P.: Multiple style transfer via variational autoencoder. In: 2021 IEEE International Conference on Image Processing (ICIP). pp. 2413–2417. IEEE (2021)
2021
Cited alongside, same era.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: Proceedings of the 38th International Conference on Machine Learning. vol. 139, pp. 8162–8171. PMLR (2021)
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Pang, Y., Lin, J., Qin, T., Chen, Z.: Image-to-image translation: Methods and applications. IEEE Transactions on Multimedia (2021)
2021
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Sharma, N., Sharma, R., Jindal, N.: Prediction of face age progression with generative adversarial networks. Multimedia Tools and Applications 80
2021
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2022
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