Explaining nonlinear classification decisions with deep Taylor decomposition
| dc.contributor.author | Montavon, Grégoire | |
| dc.contributor.author | Lapuschkin, Sebastian | |
| dc.contributor.author | Binder, Alexander | |
| dc.contributor.author | Samek, Wojciech | |
| dc.contributor.author | Müller, Klaus-Robert | |
| dc.date.accessioned | 2018-05-28T08:22:46Z | |
| dc.date.available | 2018-05-28T08:22:46Z | |
| dc.date.issued | 2017-05 | |
| dc.description.abstract | Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets. | en |
| dc.identifier.issn | 0031-3203 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/7851 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-7011 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 006 Spezielle Computerverfahren | de |
| dc.subject.ddc | 150 Psychologie | de |
| dc.subject.other | deep neural networks | en |
| dc.subject.other | heatmapping | en |
| dc.subject.other | taylor decomposition | en |
| dc.subject.other | relevance propagation | en |
| dc.subject.other | image recognition | en |
| dc.title | Explaining nonlinear classification decisions with deep Taylor decomposition | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1016/j.patcog.2016.11.008 | en |
| dcterms.bibliographicCitation.journaltitle | Pattern recognition : the journal of the Pattern Recognition Society | en |
| dcterms.bibliographicCitation.originalpublishername | Elsevier | en |
| dcterms.bibliographicCitation.originalpublisherplace | Amsterdam | en |
| dcterms.bibliographicCitation.pageend | 222 | en |
| dcterms.bibliographicCitation.pagestart | 211 | en |
| dcterms.bibliographicCitation.volume | 65 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Maschinelles Lernen | de |
| tub.affiliation.institute | Inst. Softwaretechnik und Theoretische Informatik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |