Auxiliary Losses for Learning Generalizable Concept-based Models

Ivaxi Sheth; Samira Ebrahimi Kahou

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arXiv · 2311.11108

Auxiliary Losses for Learning Generalizable Concept-based Models

Ivaxi Sheth·Samira Ebrahimi Kahou

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Abstract

The increasing use of neural networks in various applications has lead to increasing apprehensions, underscoring the necessity to understand their operations beyond mere final predictions. As a solution to enhance model transparency, Concept Bottleneck Models (CBMs) have gained popularity since their introduction. CBMs essentially limit the latent space of a model to human-understandable high-level concepts. While beneficial, CBMs have been reported to often learn irrelevant concept representations that consecutively damage model performance. To overcome the performance trade-off, we propose cooperative-Concept Bottleneck Model (coop-CBM). The concept representation of our model is particularly meaningful when fine-grained concept labels are absent. Furthermore, we introduce the concept orthogonal loss (COL) to encourage the separation between the concept representations and to reduce the intra-concept distance. This paper presents extensive experiments on real-world datasets for image classification tasks, namely CUB, AwA2, CelebA and TIL. We also study the performance of coop-CBM models under various distributional shift settings. We show that our proposed method achieves higher accuracy in all distributional shift settings even compared to the black-box models with the highest concept accuracy.

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BibTeX RIS

Ivaxi Sheth, Samira Ebrahimi Kahou. 2023-11-18. Auxiliary Losses for Learning Generalizable Concept-based Models. https://arxiv.org/abs/2311.11108

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