音频分类中局部模型不可知推理的准确性：具有对抗性示例的针对性研究
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples
摘要：
局部解释方法，如LIME已成为流行的MIR工具，用于生成事后，模型的分类决策的模型不可知论的解释。其基本思想是识别一小组人类可理解的特征的分类的例子是最有影响力的分类器的预测。这些都是作为一个解释。在出版物中对这种解释的评估往往采取接受与人类期望相匹配的内容，而实际上无法验证解释所显示的内容是否真正导致了模型的预测。本文报道了有针对性的调查，我们试图更深入地了解LIME在音频分类任务中解释的实际准确性。我们故意为分类器设计对抗性的例子，让我们知道输入的哪些部分可能对模型的（错误）预测负责。让LIME来解释对这些对手的预测，可以让我们研究局部解释是否真的能检测到这些感兴趣的区域。我们还研究了LIME是否能更成功地找到对人类来说更突出、更容易注意到的扰动。我们的研究结果表明，LIME不一定能够识别最相关的输入特征，因此仍然不清楚解释是否有用，甚至是误导。
英文摘要：
Local explanation methods such as LIME have become popular in MIR as tools for generating post-hoc, model-agnostic explanations of a model's classification decisions. The basic idea is to identify a small set of human-understandable features of the classified example that are most influential on the classifier's prediction. These are then presented as an explanation. Evaluation of such explanations in publications often resorts to accepting what matches the expectation of a human without actually being able to verify if what the explanation shows is what really caused the model's prediction. This paper reports on targeted investigations where we try to get more insight into the actual veracity of LIME's explanations in an audio classification task. We deliberately design adversarial examples for the classifier, in a way that gives us knowledge about which parts of the input are potentially responsible for the model's (wrong) prediction. Asking LIME to explain the predictions for these adversaries permits us to study whether local explanations do indeed detect these regions of interest. We also look at whether LIME is more successful in finding perturbations that are more prominent and easily noticeable for a human. Our results suggest that LIME does not necessarily manage to identify the most relevant input features and hence it remains unclear whether explanations are useful or even misleading.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
