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Diffusion models have shown exceptional scaling properties in the image synthesis domain, and initial attempts have shown similar benefits for applying diffusion to unconditional text synthesis.
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Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., Li, J., Kanda, N., Yoshioka, T., Xiao, X., et al.: WavLM: Large-scale self-supervised pre-training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing (2022)
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Chung, Y.A., Zhang, Y., Han, W., Chiu, C.C., Qin, J., Pang, R., Wu, Y.: W2v-BERT: Combining contrastive learning and masked language modeling for self-supervised speech pre-training. In: ASRU (2021)
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Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Gool, L.V.: RePaint: Inpainting using denoising diffusion probabilistic models. CoRR (2022)
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