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.
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond
Netcompany-Intrasoft S.A.（Netcompany-Intrasoft公司）；Hellenic Mediterranean University（希腊地中海大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；University of Western Macedonia（西马其顿大学）
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework
Alibaba Group（阿里巴巴集团）；The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）；Renmin University of China（中国人民大学）
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions
Information Technology R&D Center, Mitsubishi Electric Corporation（三菱电机信息科技研发中心）；Alberta Machine Intelligence Institute（阿尔伯塔人工智能研究所）；Department of Computing Science, University of Alberta（阿尔伯塔大学计算机科学系）；Advanced Technology R&D Center, Mitsubishi Electric Corporation（三菱电机先进技术研发中心）
