Abstract:
:Humans learn categories of complex objects quickly and from a few examples. Random projection has been suggested as a means to learn and categorize efficiently. We investigate how random projection affects categorization by humans and by very simple neural networks on the same stimuli and categorization tasks, and how this relates to the robustness of categories. We find that (1) drastic reduction in stimulus complexity via random projection does not degrade performance in categorization tasks by either humans or simple neural networks, (2) human accuracy and neural network accuracy are remarkably correlated, even at the level of individual stimuli, and (3) the performance of both is strongly indicated by a natural notion of category robustness.
journal_name
Neural Computjournal_title
Neural computationauthors
Arriaga RI,Rutter D,Cakmak M,Vempala SSdoi
10.1162/NECO_a_00769subject
Has Abstractpub_date
2015-10-01 00:00:00pages
2132-47issue
10eissn
0899-7667issn
1530-888Xjournal_volume
27pub_type
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