On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916081232248832 |
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| author | Davoodi, Omid Mohammadizadehsamakosh, Shayan Komeili, Majid |
| author_facet | Davoodi, Omid Mohammadizadehsamakosh, Shayan Komeili, Majid |
| contents | Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective. The proposed framework consists of three actionable metrics and experiments. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06966 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis Davoodi, Omid Mohammadizadehsamakosh, Shayan Komeili, Majid Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective. The proposed framework consists of three actionable metrics and experiments. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework. |
| title | On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2310.06966 |