2017/09/06 by Heikki Arponen, Arponen, Heikki, Matti Herranen +3 · 1 citation
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #NMR spectroscopy and applications #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1709.02797
openalex publication_date 2017/09/06 · openalex created_date 2017/09/15 · openalex updated_date 2026/07/28
We prove an exact relationship between the optimal denoising function and the data distribution in the case of additive Gaussian noise, showing that denoising implicitly models the structure of data allowing it to be exploited in the unsupervised learning of representations. This result generalizes a known relationship [2], which is valid only in the limit of small corruption noise.