Summary of Anycbms: How to Turn Any Black Box Into a Concept Bottleneck Model, by Gabriele Dominici et al.
AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
by Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Marc Langhenirich
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes “AnyCBM”, a novel method that transforms any existing trained deep learning model into an interpretable Concept Bottleneck Model (CBM) with minimal computational overhead. CBMs are neural networks whose decision-making processes can be understood by humans through the integration of human-understandable concepts. The authors highlight the limitations of traditional CBMs, which require training a new model from scratch, consuming significant resources and failing to utilize already trained large models. In contrast, AnyCBM leverages existing models, preserving their learned knowledge while gaining interpretability. Experimental results demonstrate the effectiveness of AnyCBMs in terms of classification performances and the impact of concept-based interventions on downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to make deep learning more understandable by people. They propose a new way to do this called “AnyCBM”. Right now, these interpretable models require training from scratch, which takes a lot of time and computer power. AnyCBM is special because it can take any existing trained model and turn it into an interpretable one without needing lots of resources. The authors show that AnyCBMs work well in terms of performance and helping with decision-making tasks. |
Keywords
» Artificial intelligence » Classification » Deep learning
