Authors: Seongmin Lee, Ali Payani, Duen Horng, Chau
Abstract: Modern deep learning models often make predictions by focusing on irrelevant
areas, leading to biased performance and limited generalization. Existing
methods aimed at rectifying model attention require explicit labels for
irrelevant areas or complex pixel-wise ground truth attention maps. We present
CRAYON (Correcting Reasoning with Annotations of Yes Or No), offering
effective, scalable, and practical solutions to rectify model attention using
simple yes-no annotations. CRAYON empowers classical and modern model
interpretation techniques to identify and guide model reasoning:
CRAYON-ATTENTION directs classic interpretations based on saliency maps to
focus on relevant image regions, while CRAYON-PRUNING removes irrelevant
neurons identified by modern concept-based methods to mitigate their influence.
Through extensive experiments with both quantitative and human evaluation, we
showcase CRAYON’s effectiveness, scalability, and practicality in refining
model attention. CRAYON achieves state-of-the-art performance, outperforming 12
methods across 3 benchmark datasets, surpassing approaches that require more
complex annotations.
Source: http://arxiv.org/abs/2410.22312v1
