Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
This work addresses a fundamental problem in deep learning robustness for researchers and practitioners, revealing a disconnect between two key robustness metrics, which is incremental as it builds on existing loss landscape theories.
The paper challenges the assumption that classification robustness and explanation robustness are strongly correlated in image classification systems, showing that enhancing explanation robustness does not flatten the input loss landscape for explanation loss, unlike classification robustness, and that adjustments to this landscape affect explanation robustness but not classification robustness.
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated. Through a novel evaluation approach leveraging clustering for efficient assessment of explanation robustness, we demonstrate that enhancing explanation robustness does not necessarily flatten the input loss landscape with respect to explanation loss - contrary to flattened loss landscapes indicating better classification robustness. To deeply investigate this contradiction, a groundbreaking training method designed to adjust the loss landscape with respect to explanation loss is proposed. Through the new training method, we uncover that although such adjustments can impact the robustness of explanations, they do not have an influence on the robustness of classification. These findings not only challenge the prevailing assumption of a strong correlation between the two forms of robustness but also pave new pathways for understanding relationship between loss landscape and explanation loss.