论文标题
使用贝叶斯优化技术的显着图解释的合奏聚合的保真度
Fidelity of Ensemble Aggregation for Saliency Map Explanations using Bayesian Optimization Techniques
论文作者
论文摘要
近年来，已经开发了大量解释神经网络的特征归因方法。特别是在计算机视觉领域，存在许多用于生成显着图的方法，提供像素属性。但是，他们的解释通常彼此矛盾，尚不清楚要信任哪种解释。解决此问题的一种自然解决方案是多次解释的汇总。我们介绍并将基于像素的不同聚合方案与产生新的解释进行了比较，其对模型决策的保真度高于每个单独的解释。使用贝叶斯优化领域的方法，我们将各个解释之间的方差纳入聚合过程中。此外，我们分析了多种归一化技术对集合聚集的影响。
In recent years, an abundance of feature attribution methods for explaining neural networks have been developed. Especially in the field of computer vision, many methods for generating saliency maps providing pixel attributions exist. However, their explanations often contradict each other and it is not clear which explanation to trust. A natural solution to this problem is the aggregation of multiple explanations. We present and compare different pixel-based aggregation schemes with the goal of generating a new explanation, whose fidelity to the model's decision is higher than each individual explanation. Using methods from the field of Bayesian Optimization, we incorporate the variance between the individual explanations into the aggregation process. Additionally, we analyze the effect of multiple normalization techniques on ensemble aggregation.
