Improve interpretability of Information Bottlenecks for Attribution with Layer-wise Relevance Propagation.

Researchers have developed various visualization techniques, such as attribution maps, to understand which parts of an input contribute most to a model’s decision. However, existing methods often produce disparate results and may lack human-perceptual interpretability. In this work, we propose Relevance-IBA, a novel approach that combines the strengths of Information Bottleneck Attribution (IBA) and Layer-wise Relevance Propagation’s (LRP) method to estimate more accurate and human-perceptually interpretable attribution maps. Our method accentuates the contours and subtle details of the identified object, making the model’s decisions more intuitively understandable. Additionally, we introduce a segmentation-oriented evaluation technique, which assesses the capacity of interpretability methods by emphasizing the most important pixels within an object’s boundaries. We benchmark Relevance-IBA against various methodologies, including DeepLIFT, Integrated Gradients, Guided-BP, Guided-GradCAM, IBA, and InputIBA. Our results indicate that Relevance-IBA not only boosts attribution accuracy but also prioritizes human-perceptual clarity, making it a valuable tool for interpreting complex model behaviors.