Robust deep learning object recognition models rely on low frequency information in natural images

dc.contributor.author	Rusak, Evgenia
dc.contributor.author	Brendel, Wieland
dc.contributor.author	Bethge, Matthias
dc.date.accessioned	2023-09-13T09:44:59Z
dc.date.available	2023-09-13T09:44:59Z
dc.date.issued	2023
dc.identifier.issn	1553-734X
dc.identifier.uri	http://hdl.handle.net/10900/145432
dc.language.iso	en	de_DE
dc.publisher	Public Library Science	de_DE
dc.relation.uri	http://dx.doi.org/10.1371/journal.pcbi.1010932	de_DE
dc.subject.ddc	570	de_DE
dc.subject.ddc	600	de_DE
dc.title	Robust deep learning object recognition models rely on low frequency information in natural images	de_DE
dc.type	Article	de_DE
utue.quellen.id	20230619000000_00950
utue.personen.roh	Li, Zhe
utue.personen.roh	Caro, Josue Ortega
utue.personen.roh	Rusak, Evgenia
utue.personen.roh	Brendel, Wieland
utue.personen.roh	Bethge, Matthias
utue.personen.roh	Anselmi, Fabio
utue.personen.roh	Patel, Ankit
utue.personen.roh	Tolias, Andreas
utue.personen.roh	Pitkow, Xaq
dcterms.isPartOf.ZSTitelID	Plos Computational Biology	de_DE
dcterms.isPartOf.ZS-Issue	3	de_DE
dcterms.isPartOf.ZS-Volume	19	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät	de_DE
utue.fakultaet	08 Zentrale, interfakultäre und fakultätsübergreifende Einrichtungen	de_DE