DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning Article Swipe
Changkyu Choi
,
Shujian Yu
,
Michael Kampffmeyer
,
Arnt-Børre Salberg
,
Nils Olav Handegard
,
Robert Jenssen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/icassp48485.2024.10447094
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/icassp48485.2024.10447094
The recent development of self-explainable deep learning approaches has focused on integrating well-defined explainability principles into learning process, with the goal of achieving these principles through optimization. In this work, we propose DIB-X, a self-explainable deep learning approach for image data, which adheres to the principles of minimal, sufficient, and interactive explanations. The minimality and sufficiency principles are rooted from the trade-off relationship within the information bottleneck framework. Distinctly, DIB-X directly quantifies the minimality principle using the recently proposed matrix-based Rényi's α-order entropy functional, circumventing the need for variational approximation and distributional assumption. The interactivity principle is realized by incorporating existing domain knowledge as prior explanations, fostering explanations that align with established domain understanding. Empirical results on MNIST and two marine environment monitoring datasets with different modalities reveal that our approach primarily provides improved explainability with the added advantage of enhanced classification performance.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icassp48485.2024.10447094
- OA Status
- green
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- 1
- References
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- OpenAlex ID
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https://openalex.org/W4392910497Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icassp48485.2024.10447094Digital Object Identifier
- Title
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DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic LearningWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-18Full publication date if available
- Authors
-
Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Robert JenssenList of authors in order
- Landing page
-
https://doi.org/10.1109/icassp48485.2024.10447094Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://research.vu.nl/en/publications/6926f27a-b527-4284-9063-1294a754f843Direct OA link when available
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Computer science, Artificial intelligence, Cognitive science, PsychologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 19, 44, 60, 64, 72, 76, 85, 136 |
| abstract_inverted_index.two | 119 |
| abstract_inverted_index.deep | 5, 35 |
| abstract_inverted_index.from | 59 |
| abstract_inverted_index.goal | 20 |
| abstract_inverted_index.into | 15 |
| abstract_inverted_index.need | 86 |
| abstract_inverted_index.that | 108, 128 |
| abstract_inverted_index.this | 28 |
| abstract_inverted_index.with | 18, 110, 124, 135 |
| abstract_inverted_index.DIB-X | 69 |
| abstract_inverted_index.MNIST | 117 |
| abstract_inverted_index.added | 137 |
| abstract_inverted_index.align | 109 |
| abstract_inverted_index.data, | 40 |
| abstract_inverted_index.image | 39 |
| abstract_inverted_index.prior | 104 |
| abstract_inverted_index.these | 23 |
| abstract_inverted_index.using | 75 |
| abstract_inverted_index.which | 41 |
| abstract_inverted_index.work, | 29 |
| abstract_inverted_index.DIB-X, | 32 |
| abstract_inverted_index.domain | 101, 112 |
| abstract_inverted_index.marine | 120 |
| abstract_inverted_index.recent | 1 |
| abstract_inverted_index.reveal | 127 |
| abstract_inverted_index.rooted | 58 |
| abstract_inverted_index.within | 63 |
| abstract_inverted_index.adheres | 42 |
| abstract_inverted_index.entropy | 82 |
| abstract_inverted_index.focused | 9 |
| abstract_inverted_index.propose | 31 |
| abstract_inverted_index.results | 115 |
| abstract_inverted_index.through | 25 |
| abstract_inverted_index.Rényi's | 80 |
| abstract_inverted_index.approach | 37, 130 |
| abstract_inverted_index.datasets | 123 |
| abstract_inverted_index.directly | 70 |
| abstract_inverted_index.enhanced | 140 |
| abstract_inverted_index.existing | 100 |
| abstract_inverted_index.improved | 133 |
| abstract_inverted_index.learning | 6, 16, 36 |
| abstract_inverted_index.minimal, | 47 |
| abstract_inverted_index.process, | 17 |
| abstract_inverted_index.proposed | 78 |
| abstract_inverted_index.provides | 132 |
| abstract_inverted_index.realized | 97 |
| abstract_inverted_index.recently | 77 |
| abstract_inverted_index.α-order | 81 |
| abstract_inverted_index.Empirical | 114 |
| abstract_inverted_index.achieving | 22 |
| abstract_inverted_index.advantage | 138 |
| abstract_inverted_index.different | 125 |
| abstract_inverted_index.fostering | 106 |
| abstract_inverted_index.knowledge | 102 |
| abstract_inverted_index.primarily | 131 |
| abstract_inverted_index.principle | 74, 95 |
| abstract_inverted_index.trade-off | 61 |
| abstract_inverted_index.approaches | 7 |
| abstract_inverted_index.bottleneck | 66 |
| abstract_inverted_index.framework. | 67 |
| abstract_inverted_index.minimality | 53, 73 |
| abstract_inverted_index.modalities | 126 |
| abstract_inverted_index.monitoring | 122 |
| abstract_inverted_index.principles | 14, 24, 45, 56 |
| abstract_inverted_index.quantifies | 71 |
| abstract_inverted_index.Distinctly, | 68 |
| abstract_inverted_index.assumption. | 92 |
| abstract_inverted_index.development | 2 |
| abstract_inverted_index.environment | 121 |
| abstract_inverted_index.established | 111 |
| abstract_inverted_index.functional, | 83 |
| abstract_inverted_index.information | 65 |
| abstract_inverted_index.integrating | 11 |
| abstract_inverted_index.interactive | 50 |
| abstract_inverted_index.sufficiency | 55 |
| abstract_inverted_index.sufficient, | 48 |
| abstract_inverted_index.variational | 88 |
| abstract_inverted_index.explanations | 107 |
| abstract_inverted_index.matrix-based | 79 |
| abstract_inverted_index.performance. | 142 |
| abstract_inverted_index.relationship | 62 |
| abstract_inverted_index.well-defined | 12 |
| abstract_inverted_index.approximation | 89 |
| abstract_inverted_index.circumventing | 84 |
| abstract_inverted_index.explanations, | 105 |
| abstract_inverted_index.explanations. | 51 |
| abstract_inverted_index.incorporating | 99 |
| abstract_inverted_index.interactivity | 94 |
| abstract_inverted_index.optimization. | 26 |
| abstract_inverted_index.classification | 141 |
| abstract_inverted_index.distributional | 91 |
| abstract_inverted_index.explainability | 13, 134 |
| abstract_inverted_index.understanding. | 113 |
| abstract_inverted_index.self-explainable | 4, 34 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.65448653 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |