Model Guidance via Explanations Turns Image Classifiers into Segmentation Models

Explainability methods can serve dual purposes: Techniques like Grad-CAM and LRP, which are designed to make AI decisions more interpretable to humans, can simultaneously be repurposed to improve model performance on segmentation tasks—demonstrating that transparency and accuracy aren't necessarily at odds.

"Right for the right reasons" approach reduces reliance on labeled data: By imposing constraints on heatmaps to ensure models focus on relevant features rather than confounding ones, the researchers showed that models can achieve competitive segmentation results using fewer pixel-level labels combined with image-level labels, potentially reducing expensive annotation costs.

Interpretability constraints act as built-in safeguards: The paper demonstrates that explicitly training models to ignore confounding input features (through heatmap-based losses) helps prevent models from making decisions based on spurious correlations, which is critical for responsible AI deployment in sensitive applications.

Architectural equivalence between interpretability and segmentation: The formal parallels identified between differentiable heatmap architectures and standard segmentation models suggest that building interpretability into a model's core design—rather than adding it as an afterthought—can be both technically sound and practically beneficial.