Faithful Counterfactual Visual Explanations (FCVE)
Bismillah Khan, Syed Ali Tariq, Tehseen Zia, Muhammad Ahsan, David, Windridge

TL;DR
This paper introduces a counterfactual explanation model for deep learning in computer vision that produces faithful, plausible, and easy-to-understand visual explanations by identifying internal concepts and making minimal necessary changes.
Contribution
It presents a novel counterfactual explanation method that balances plausibility and faithfulness, improving interpretability of deep vision models.
Findings
Produces visual explanations reflecting internal decision processes
Balances plausibility and faithfulness in explanations
Enhances interpretability of deep learning models
Abstract
Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models' intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-to-understand visual explanations by making minimum changes necessary in images without altering the pixel data. Instead, the proposed method identifies internal concepts and filters learned by models and leverages them to produce plausible counterfactual explanations. The provided explanations…
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