Faithful Counterfactual Visual Explanations (FCVE)

2024-03-01

Knowledge-Based Systems

AI Summary

Deep learning models in computer vision suffer from opaque decision-making processes and poor interpretability, hindering comprehension by non-experts. To address this, we propose a novel method for generating high-fidelity counterfactual image explanations. Our approach is the first to formally define and optimize a “faithfulness” metric—ensuring explanations strictly reflect the model’s true decision boundary. We integrate gradient-guided adversarial perturbations, latent-space constrained optimization, and a differentiable approximation of the classification boundary within an end-to-end differentiable framework, enabling pixel-level minimal modifications. Evaluated on ImageNet and CUB, our method improves explanation faithfulness by 23.6% over state-of-the-art methods, while significantly enhancing human interpretability. This facilitates fine-grained model diagnosis and debugging.

Technology Category

Computer Vision: Interpretability, Explainability, and TransparencyHumans and AI: Explainable AI (XAI) for Human UnderstandingMachine Learning: Adversarial Learning & Robustness

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationResponsible Web: Human-perceived consequences of algorithmic deployment on the web

Problem

• Interpretability
• Deep Learning
• Computer Vision

Innovation

• Counterfactual Explanations
• Computer Vision
• Interpretability