论文标题
Neuron Shapley：发现负责任的神经元
Neuron Shapley: Discovering the Responsible Neurons
论文作者
论文摘要
我们开发神经元莎普利作为一个新框架，以量化单个神经元对深网的预测和性能的贡献。通过考虑神经元之间的相互作用，与基于激活模式相比，神经元沙普利在识别重要过滤器方面更有效。有趣的是，仅删除Shapley得分最高的30个过滤器可以有效地破坏Inception-V3在Imagenet上的预测准确性。这几个关键过滤器的可视化提供了有关网络功能的见解。 Neuron Shapley是一个灵活的框架，可以应用于在许多任务中识别负责任的神经元。我们说明了识别导致面部识别和过滤器中有偏见预测的过滤器的其他应用程序，这些预测容易受到对抗攻击的影响。去除这些过滤器是修复模型的快速方法。启用所有这些应用程序是一种新的多臂强盗算法，我们开发了该算法，以有效地估计神经元Shapley值。
We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions across neurons, Neuron Shapley is more effective in identifying important filters compared to common approaches based on activation patterns. Interestingly, removing just 30 filters with the highest Shapley scores effectively destroys the prediction accuracy of Inception-v3 on ImageNet. Visualization of these few critical filters provides insights into how the network functions. Neuron Shapley is a flexible framework and can be applied to identify responsible neurons in many tasks. We illustrate additional applications of identifying filters that are responsible for biased prediction in facial recognition and filters that are vulnerable to adversarial attacks. Removing these filters is a quick way to repair models. Enabling all these applications is a new multi-arm bandit algorithm that we developed to efficiently estimate Neuron Shapley values.
