AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910460093136896 |
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| author | Dominici, Gabriele Barbiero, Pietro Giannini, Francesco Gjoreski, Martin Langhenirich, Marc |
| author_facet | Dominici, Gabriele Barbiero, Pietro Giannini, Francesco Gjoreski, Martin Langhenirich, Marc |
| contents | Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users. Among these techniqes, Concept Bottleneck Models enhance the interpretability of neural networks by integrating a layer of human-understandable concepts. These models, however, necessitate training a new model from the beginning, consuming significant resources and failing to utilize already trained large models. To address this issue, we introduce "AnyCBM", a method that transforms any existing trained model into a Concept Bottleneck Model with minimal impact on computational resources. We provide both theoretical and experimental insights showing the effectiveness of AnyCBMs in terms of classification performances and effectivenss of concept-based interventions on downstream tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16508 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model Dominici, Gabriele Barbiero, Pietro Giannini, Francesco Gjoreski, Martin Langhenirich, Marc Machine Learning Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users. Among these techniqes, Concept Bottleneck Models enhance the interpretability of neural networks by integrating a layer of human-understandable concepts. These models, however, necessitate training a new model from the beginning, consuming significant resources and failing to utilize already trained large models. To address this issue, we introduce "AnyCBM", a method that transforms any existing trained model into a Concept Bottleneck Model with minimal impact on computational resources. We provide both theoretical and experimental insights showing the effectiveness of AnyCBMs in terms of classification performances and effectivenss of concept-based interventions on downstream tasks. |
| title | AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.16508 |