Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks Article Swipe
Qihan Huang
,
Mengqi Xue
,
Haofei Zhang
,
Jie Song
,
Mingli Song
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.05946
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.05946
Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the semantic gap between the similarities in the feature space and that in the input space. In this work, we strive to address this challenge by making the first attempt to quantitatively and objectively evaluate the interpretability of the part-prototype networks. Specifically, we propose two evaluation metrics, termed as consistency score and stability score, to evaluate the explanation consistency across images and the explanation robustness against perturbations, respectively, both of which are essential for explanations taken into practice. Furthermore, we propose an elaborated part-prototype network with a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and provide substantial discussions to uncover the interpretability of existing part-prototype networks. Experiments on three benchmarks across nine architectures demonstrate that our model achieves significantly superior performance to the state of the art, in both the accuracy and interpretability. Our code is available at https://github.com/hqhQAQ/EvalProtoPNet.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.05946
- https://arxiv.org/pdf/2212.05946
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311431240
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311431240Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2212.05946Digital Object Identifier
- Title
-
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-12Full publication date if available
- Authors
-
Qihan Huang, Mengqi Xue, Haofei Zhang, Jie Song, Mingli SongList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.05946Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2212.05946Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2212.05946Direct OA link when available
- Concepts
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Interpretability, Computer science, Robustness (evolution), Consistency (knowledge bases), Machine learning, Artificial intelligence, Feature (linguistics), Code (set theory), Data mining, Programming language, Set (abstract data type), Gene, Chemistry, Biochemistry, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ProtoTree, | 4 |
| abstract_inverted_index.benchmarks | 159 |
| abstract_inverted_index.comparable | 17 |
| abstract_inverted_index.elaborated | 117 |
| abstract_inverted_index.evaluation | 80, 142 |
| abstract_inverted_index.prototypes | 30 |
| abstract_inverted_index.robustness | 99 |
| abstract_inverted_index.Experiments | 156 |
| abstract_inverted_index.aggregation | 130 |
| abstract_inverted_index.consistency | 84, 93 |
| abstract_inverted_index.demonstrate | 163 |
| abstract_inverted_index.discussions | 147 |
| abstract_inverted_index.experiments | 143 |
| abstract_inverted_index.explanation | 92, 98 |
| abstract_inverted_index.objectively | 68 |
| abstract_inverted_index.performance | 170 |
| abstract_inverted_index.prototypes. | 138 |
| abstract_inverted_index.substantial | 146 |
| abstract_inverted_index.Furthermore, | 113 |
| abstract_inverted_index.explanations | 109 |
| abstract_inverted_index.shallow-deep | 122 |
| abstract_inverted_index.similarities | 40 |
| abstract_inverted_index.systematical | 141 |
| abstract_inverted_index.Specifically, | 76 |
| abstract_inverted_index.architectures | 162 |
| abstract_inverted_index.counterparts. | 21 |
| abstract_inverted_index.respectively, | 102 |
| abstract_inverted_index.significantly | 168 |
| abstract_inverted_index.Part-prototype | 0 |
| abstract_inverted_index.part-prototype | 74, 118, 154 |
| abstract_inverted_index.perturbations, | 101 |
| abstract_inverted_index.quantitatively | 66 |
| abstract_inverted_index.interpretability | 15, 28, 71, 136, 151 |
| abstract_inverted_index.interpretability. | 182 |
| abstract_inverted_index.non-interpretable | 20 |
| abstract_inverted_index.https://github.com/hqhQAQ/EvalProtoPNet. | 188 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |