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Inpainting and Denoising Challenges

The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/v...

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Corporate Author: SpringerLink (Online service)
Other Authors: Escalera, Sergio. (Editor, http://id.loc.gov/vocabulary/relators/edt), Ayache, Stephane. (Editor, http://id.loc.gov/vocabulary/relators/edt), Wan, Jun. (Editor, http://id.loc.gov/vocabulary/relators/edt), Madadi, Meysam. (Editor, http://id.loc.gov/vocabulary/relators/edt), Güçlü, Umut. (Editor, http://id.loc.gov/vocabulary/relators/edt), Baró, Xavier. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:The Springer Series on Challenges in Machine Learning,
Subjects:
Artificial intelligence.
Optical data processing.
Pattern recognition.
Artificial Intelligence.
Image Processing and Computer Vision.
Pattern Recognition.
Online Access:https://doi.org/10.1007/978-3-030-25614-2

Internet

https://doi.org/10.1007/978-3-030-25614-2

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