AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model

• URL: http://arxiv.org/abs/2405.16508v1
• Date: Sun, 26 May 2024 10:19:04 GMT
• Title: AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
• Authors: Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Marc Langhenirich,
• Abstract summary: Concept Bottleneck Models enhance the interpretability of neural networks by integrating a layer of human-understandable concepts. "AnyCBM" transforms any existing trained model into a Concept Bottleneck Model with minimal impact on computational resources.
• Score: 7.674744385997066
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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