Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
arXiv:2403.06013v2 Announce Type: replace Abstract: 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 ro
This research addresses a fundamental assumption in deep learning robustness, published as the field continues to rapidly advance its understanding of AI system reliability and interpretability.
It challenges the conventional wisdom regarding the correlation between classification and explanation robustness, which is critical for developing more trustworthy and reliable AI systems, especially in high-stakes applications.
The understanding that improving explanation robustness does not automatically lead to better classification robustness or flattened loss landscapes, suggesting that these two aspects of AI robustness may need to be addressed more independently.
- · AI researchers focusing on explainability
- · Developers of robust AI systems
- · Sectors requiring high AI trustworthiness
- · AI development relying on assumed correlations
- · AI systems without explicit explanation robustness measures
Increased focus on disentangling classification and explanation robustness in AI research and development.
Development of new metrics and methodologies for independently evaluating and improving both aspects of robustness.
Potentially slower, more complex AI development as systems require dual-track optimization for robustness and explainability.
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Read at arXiv cs.LG