Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
<|reference_start|>Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape: 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.<|reference_end|>
@article{chen2024are,
    title={Are Classification Robustness and Explanation Robustness Really Strongly
  Correlated? An Analysis Through Input Loss Landscape},
    author={Tiejin Chen, Wenwang Huang, Linsey Pang, Dongsheng Luo, Hua Wei},
    journal={arXiv preprint arXiv:2403.06013},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06013},
    primaryClass={cs.LG cs.CV}
}