Learning Nonlinear Statistical Regularities in Natural Images by Modeling the Outer Product of Image Intensities

DOI10.1162/NECO_A_00567zbMath1417.91422OpenAlexW2143720523WikidataQ50692240 ScholiaQ50692240MaRDI QIDQ5378339

Peng Qi, Xiaolin Hu

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00567

zbMATH Keywords

learningprimary visual cortexnatural images perceptionnonlinear statistical regularities

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20) Memory and learning in psychology (91E40) Psychophysics and psychophysiology; perception (91E30)

Uses Software

• STL-10 dataset

Cites Work

• Natural image statistics. A probabilistic approach to early computational vision.
• Topographic Independent Component Analysis
• A Two-Layer Model of Natural Stimuli Estimated with Score Matching
• Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics
• Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes
• A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals

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