GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps Article Swipe
Oren Barkan
,
Omri Armstrong
,
Amir Hertz
,
Avi Caciularu
,
Ori Katz
,
Itzik Malkiel
,
Noam Koenigstein
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.00951
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.00951
We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual explanations, when compared to existing alternatives. The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2109.00951
- https://arxiv.org/pdf/2109.00951
- OA Status
- green
- Cited By
- 1
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3197387866
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Raw OpenAlex JSON
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https://openalex.org/W3197387866Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2109.00951Digital Object Identifier
- Title
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GAM: Explainable Visual Similarity and Classification via Gradient Activation MapsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-02Full publication date if available
- Authors
-
Oren Barkan, Omri Armstrong, Amir Hertz, Avi Caciularu, Ori Katz, Itzik Malkiel, Noam KoenigsteinList of authors in order
- Landing page
-
https://arxiv.org/abs/2109.00951Publisher landing page
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https://arxiv.org/pdf/2109.00951Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.00951Direct OA link when available
- Concepts
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Similarity (geometry), Computer science, Artificial intelligence, Generalized additive model, Pattern recognition (psychology), Machine learning, Data mining, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
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24Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2021-09-02 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W3090578762, https://openalex.org/W2141200610, https://openalex.org/W2963953476, https://openalex.org/W2962851944, https://openalex.org/W2194775991, https://openalex.org/W1861492603, https://openalex.org/W2144172034, https://openalex.org/W1825675169, https://openalex.org/W3104119469, https://openalex.org/W3022730539, https://openalex.org/W2962858109, https://openalex.org/W2963775347, https://openalex.org/W2135442311, https://openalex.org/W2963026686, https://openalex.org/W2031489346, https://openalex.org/W2963125676, https://openalex.org/W3208147779, https://openalex.org/W2123045220, https://openalex.org/W2112796928, https://openalex.org/W2963166708, https://openalex.org/W2770241596, https://openalex.org/W1692173902, https://openalex.org/W2891612330, https://openalex.org/W2963446712 |
| referenced_works_count | 24 |
| abstract_inverted_index.- | 6 |
| abstract_inverted_index.a | 7 |
| abstract_inverted_index.By | 19 |
| abstract_inverted_index.We | 0 |
| abstract_inverted_index.by | 13 |
| abstract_inverted_index.in | 47 |
| abstract_inverted_index.is | 54 |
| abstract_inverted_index.it | 53 |
| abstract_inverted_index.of | 43 |
| abstract_inverted_index.to | 37 |
| abstract_inverted_index.GAM | 30, 44, 57 |
| abstract_inverted_index.The | 40 |
| abstract_inverted_index.and | 16, 23, 49, 64 |
| abstract_inverted_index.are | 45 |
| abstract_inverted_index.for | 9 |
| abstract_inverted_index.its | 59 |
| abstract_inverted_index.Maps | 4 |
| abstract_inverted_index.from | 26 |
| abstract_inverted_index.made | 12 |
| abstract_inverted_index.that | 56 |
| abstract_inverted_index.when | 35 |
| abstract_inverted_index.(GAM) | 5 |
| abstract_inverted_index.shown | 55 |
| abstract_inverted_index.tasks | 63 |
| abstract_inverted_index.where | 52 |
| abstract_inverted_index.across | 61 |
| abstract_inverted_index.offers | 31 |
| abstract_inverted_index.visual | 14, 33 |
| abstract_inverted_index.detail, | 48 |
| abstract_inverted_index.layers, | 29 |
| abstract_inverted_index.models. | 18 |
| abstract_inverted_index.network | 28 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.various | 62 |
| abstract_inverted_index.Gradient | 2 |
| abstract_inverted_index.compared | 36 |
| abstract_inverted_index.existing | 38 |
| abstract_inverted_index.gleaning | 20 |
| abstract_inverted_index.gradient | 22 |
| abstract_inverted_index.improved | 32 |
| abstract_inverted_index.multiple | 27 |
| abstract_inverted_index.datasets. | 65 |
| abstract_inverted_index.explained | 46 |
| abstract_inverted_index.localized | 21 |
| abstract_inverted_index.machinery | 8 |
| abstract_inverted_index.validated | 50 |
| abstract_inverted_index.Activation | 3 |
| abstract_inverted_index.activation | 24 |
| abstract_inverted_index.advantages | 42 |
| abstract_inverted_index.explaining | 10 |
| abstract_inverted_index.similarity | 15 |
| abstract_inverted_index.algorithmic | 41 |
| abstract_inverted_index.information | 25 |
| abstract_inverted_index.outperforms | 58 |
| abstract_inverted_index.predictions | 11 |
| abstract_inverted_index.alternatives | 60 |
| abstract_inverted_index.empirically, | 51 |
| abstract_inverted_index.alternatives. | 39 |
| abstract_inverted_index.explanations, | 34 |
| abstract_inverted_index.classification | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |