通过聚类神经元和变异体，大幅加速深度神经网络测试分析。
On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering
- 用相似权重的神经元分组减少生成的变异体数量。
- 通过选择代表性变异体测试，平均提速35.31%且误差仅1.96%。
- 适合需要高效评估模型鲁棒性的研究人员使用。
深度神经网络（DNN）的变异分析是评估测试数据质量与模型鲁棒性的有效方法，但计算成本高，尤其对大型模型。本文提出DEEPMAACC技术与工具，通过神经元和变异体聚类加速分析。该方法包含：(1) 神经元聚类以减少生成的变异体数量；(2) 变异体聚类通过选取代表性变异体降低测试量。两者均采用层次凝聚聚类，基于相似权重分组。在4个主流分类数据集上，针对8个DNN模型、两种架构进行评估。相比全量变异分析，神经元聚类平均提速69.77%，变异得分误差-26.84%；变异体聚类平均提速35.31%，得分误差仅1.96%。结果表明可在速度与得分误差间实现可接受权衡。
原文摘要 · Abstract (English)
Mutation analysis of deep neural networks (DNNs) is a promising method for effective evaluation of test data quality and model robustness, but it can be computationally expensive, especially for large models. To alleviate this, we present DEEPMAACC, a technique and a tool that speeds up DNN mutation analysis through neuron and mutant clustering. DEEPMAACC implements two methods: (1) neuron clustering to reduce the number of generated mutants and (2) mutant clustering to reduce the number of mutants to be tested by selecting representative mutants for testing. Both use hierarchical agglomerative clustering to group neurons and mutants with similar weights, with the goal of improving efficiency while maintaining mutation score. DEEPMAACC has been evaluated on 8 DNN models across 4 popular classification datasets and two DNN architectures. When compared to exhaustive, or vanilla, mutation analysis, the results provide empirical evidence that neuron clustering approach, on average, accelerates mutation analysis by 69.77%, with an average -26.84% error in mutation score. Meanwhile, mutant clustering approach, on average, accelerates mutation analysis by 35.31%, with an average 1.96% error in mutation score. Our results demonstrate that a trade-off can be made between mutation testing speed and mutation score error.
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