Improving interpretability via regularization of neural activation sensitivity.
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| Title: | Improving interpretability via regularization of neural activation sensitivity. |
|---|---|
| Authors: | Moshe, Ofir1 (AUTHOR), Fidel, Gil1 (AUTHOR), Bitton, Ron1 (AUTHOR), Shabtai, Asaf1 (AUTHOR) shabtaia@bgu.ac.il |
| Source: | Machine Learning. Sep2024, Vol. 113 Issue 9, p6165-6196. 32p. |
| Subjects: | Artificial neural networks, Stimulus generalization, Trust, Confidence |
| Abstract: | State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in mission-critical contexts is limited due to two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about DNNs' security and generalization in real-world conditions, while the latter, opaqueness, directly impacts interpretability. The lack of interpretability diminishes user trust as it is challenging to have confidence in a model's decision when its reasoning is not aligned with human perspectives. In this research, we (1) examine the effect of adversarial robustness on interpretability, and (2) present a novel approach for improving DNNs' interpretability that is based on the regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models, and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability.(Code provided in supplementary material.) [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 178877138 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improving interpretability via regularization of neural activation sensitivity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moshe%2C+Ofir%22">Moshe, Ofir</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fidel%2C+Gil%22">Fidel, Gil</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bitton%2C+Ron%22">Bitton, Ron</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shabtai%2C+Asaf%22">Shabtai, Asaf</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shabtaia@bgu.ac.il</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. Sep2024, Vol. 113 Issue 9, p6165-6196. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Stimulus+generalization%22">Stimulus generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Trust%22">Trust</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence%22">Confidence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in mission-critical contexts is limited due to two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about DNNs' security and generalization in real-world conditions, while the latter, opaqueness, directly impacts interpretability. The lack of interpretability diminishes user trust as it is challenging to have confidence in a model's decision when its reasoning is not aligned with human perspectives. In this research, we (1) examine the effect of adversarial robustness on interpretability, and (2) present a novel approach for improving DNNs' interpretability that is based on the regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models, and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability.(Code provided in supplementary material.) [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=178877138 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10994-024-06549-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 6165 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Stimulus generalization Type: general – SubjectFull: Trust Type: general – SubjectFull: Confidence Type: general Titles: – TitleFull: Improving interpretability via regularization of neural activation sensitivity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moshe, Ofir – PersonEntity: Name: NameFull: Fidel, Gil – PersonEntity: Name: NameFull: Bitton, Ron – PersonEntity: Name: NameFull: Shabtai, Asaf IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 113 – Type: issue Value: 9 Titles: – TitleFull: Machine Learning Type: main |
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