Auxiliary Losses for Learning Generalizable Concept-based Models
Ivaxi Sheth Samira Ebrahimi Kahou
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.