论文标题
具有归因平衡的神经元激活中显着贡献的显着贡献
Illuminating Salient Contributions in Neuron Activation with Attribution Equilibrium
论文作者
论文摘要
随着深层神经网络的显着成功，人们对研究的兴趣越来越大，旨在为其决策过程提供明确的解释。在本文中，我们介绍了归因均衡，这是一种将输出预测分解为细粒归因的新方法，平衡了积极和负相关，以更清晰地可视化网络决策背后的证据。我们仔细分析了传统方法以进行决策解释，并就证据的保护提出了不同的观点。我们将证据定义为梯度衍生的初始贡献图之间的正面和负面影响之间的差距。然后，我们将拮抗元素和用户定义的标准结合在一起，以实现传播过程中积极归因的程度。此外，我们考虑了灭活神经元在传播规则中的作用，从而增强了对背景等较少相关因素的识别。我们使用Pascal VOC 2007，Coco 2014和Imagenet数据集进行了经过验证的实验环境中进行各种评估。结果表明，我们的方法在识别影响模型决策的关键输入特征方面优于现有归因方法。
With the remarkable success of deep neural networks, there is a growing interest in research aimed at providing clear interpretations of their decision-making processes. In this paper, we introduce Attribution Equilibrium, a novel method to decompose output predictions into fine-grained attributions, balancing positive and negative relevance for clearer visualization of the evidence behind a network decision. We carefully analyze conventional approaches to decision explanation and present a different perspective on the conservation of evidence. We define the evidence as a gap between positive and negative influences among gradient-derived initial contribution maps. Then, we incorporate antagonistic elements and a user-defined criterion for the degree of positive attribution during propagation. Additionally, we consider the role of inactivated neurons in the propagation rule, thereby enhancing the discernment of less relevant elements such as the background. We conduct various assessments in a verified experimental environment with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing attribution methods both qualitatively and quantitatively in identifying the key input features that influence model decisions.
