剪枝让车用AI更透明，解释更可靠。
Smaller is Better: Enhancing Transparency in Vehicle AI Systems via Pruning
- 通过剪枝使模型特征更稀疏，提升可解释性。
- 剪枝后显著提高显著图的忠实度和可读性。
- 适合资源受限的自动驾驶系统开发使用。
联网与自动驾驶汽车持续依赖人工智能系统，其透明性与安全性对建立信任和保障运行安全至关重要。事后解释虽能揭示黑箱模型决策，但常因不一致性和忠实度不足而受到质疑。本文系统评估了自然训练、对抗训练与剪枝三种常用训练方法对交通标志分类器事后解释质量的影响。大量实验证明，剪枝显著提升了显著图的可理解性与忠实度。研究发现，剪枝不仅提高模型效率，还促使学习表征产生稀疏性，使决策更可解释、更可靠。这些发现表明，剪枝是构建透明深度学习模型的有前景策略，尤其适用于资源受限的车载AI系统。
原文摘要 · Abstract (English)
Connected and autonomous vehicles continue to heavily rely on AI systems, where transparency and security are critical for trust and operational safety. Post-hoc explanations provide transparency to these black-box like AI models but the quality and reliability of these explanations is often questioned due to inconsistencies and lack of faithfulness in representing model decisions. This paper systematically examines the impact of three widely used training approaches, namely natural training, adversarial training, and pruning, affect the quality of post-hoc explanations for traffic sign classifiers. Through extensive empirical evaluation, we demonstrate that pruning significantly enhances the comprehensibility and faithfulness of explanations (using saliency maps). Our findings reveal that pruning not only improves model efficiency but also enforces sparsity in learned representation, leading to more interpretable and reliable decisions. Additionally, these insights suggest that pruning is a promising strategy for developing transparent deep learning models, especially in resource-constrained vehicular AI systems.
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