The LRP Toolbox for Artificial Neural Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lapuschkin, Sebastian | - |
dc.contributor.author | Binder, Alexander | - |
dc.contributor.author | Montavon, Gregoire | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Samek, Wojciech | - |
dc.date.accessioned | 2021-09-04T05:06:30Z | - |
dc.date.available | 2021-09-04T05:06:30Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 1532-4435 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/90193 | - |
dc.description.abstract | The Layer-wise Relevance Propagation (LRP) algorithm explains a classifier's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself. With the LRP Toolbox we provide platform-agnostic implementations for explaining the predictions of pre-trained state of the art Caffe networks and stand-alone implementations for fully connected Neural Network models. The implementations for Matlab and python shall serve as a playing field to familiarize oneself with the LRP algorithm and are implemented with readability and transparency in mind. Models and data can be imported and exported using raw text formats, Matlab's. mat files and the. npy format for numpy or plain text. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MICROTOME PUBL | - |
dc.title | The LRP Toolbox for Artificial Neural Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.scopusid | 2-s2.0-84989211382 | - |
dc.identifier.wosid | 000391550900001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF MACHINE LEARNING RESEARCH, v.17 | - |
dc.relation.isPartOf | JOURNAL OF MACHINE LEARNING RESEARCH | - |
dc.citation.title | JOURNAL OF MACHINE LEARNING RESEARCH | - |
dc.citation.volume | 17 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordAuthor | layer-wise relevance propagation | - |
dc.subject.keywordAuthor | explaining classifiers | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | artificial neural networks | - |
dc.subject.keywordAuthor | computer vision | - |
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