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pro vyhledávání: '"Emanuele Aiello"'
Publikováno v:
IEEE Access, Vol 12, Pp 106912-106923 (2024)
Denoising Diffusion Models (DDMs) have become a popular tool for generating high-quality samples from complex data distributions. These models are able to capture sophisticated patterns and structures in the data, and can generate samples that are hi
Externí odkaz:
https://doaj.org/article/4828050d99b9499691f051b18df6e917
Autor:
Emanuele Aiello, Mirko Agarla, Diego Valsesia, Paolo Napoletano, Tiziano Bianchi, Enrico Magli, Raimondo Schettini
Publikováno v:
IEEE Access, Vol 12, Pp 65024-65031 (2024)
Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural designs. How
Externí odkaz:
https://doaj.org/article/00158747af4643a2aaac44e8708bc100