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10.1073/pnas.1513198113 http://scihub22266oqcxt.onion/10.1073/pnas.1513198113 C4790978!4790978!26884200     free     free     free       Proc+Natl+Acad+Sci+U+S+A 2016 ; 113 (10): 2744-9 Nephropedia Template TP {{tp|p=26884200|t=2016. Atoms of recognition in human and computer vision |pdf=|usr=}}{{26884200}} gab.com Text Atoms of recognition in human and computer vision JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26884200 #FOAvip Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recent successes in computational models of visual recognition naturally raise the question: Do computer systems and the human brain use similar or different computations? We show by combining a novel method (minimal images) and simulations that the human recognition system uses features and learning processes, which are critical for recognition, but are not used by current models. The study uses a ?phase transition? phenomenon in minimal images, in which minor changes to the image have a drastic effect on its recognition. The results show fundamental limitations of current approaches and suggest directions to produce more realistic and better-performing models. Twit Text FOAVip Atoms of recognition in human and computer vision ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26884200 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26884200 #FOAvip English Wikipedia <ref>{{Cite pmid|26884200}}</ref> Atoms of recognition in human and computer vision #MMPMID26884200 Ullman S; Assif L; Fetaya E; Harari D Proc Natl Acad Sci U S A 2016[Mar]; 113 (10): 2744-9 PMID26884200show ga Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recent successes in computational models of visual recognition naturally raise the question: Do computer systems and the human brain use similar or different computations? We show by combining a novel method (minimal images) and simulations that the human recognition system uses features and learning processes, which are critical for recognition, but are not used by current models. The study uses a ?phase transition? phenomenon in minimal images, in which minor changes to the image have a drastic effect on its recognition. The results show fundamental limitations of current approaches and suggest directions to produce more realistic and better-performing models. äAtoms of recognition in human and computer vision (epmc).pdf DeepDyve Pubget Overpricing