论文标题
高阶共同信息揭示了多个神经元的协同子网络
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance
论文作者
论文摘要
量化哪些神经元在训练有素的神经网络的分类决策方面很重要，这对于理解其内部起作用至关重要。先前的工作主要归因于个体神经元。在这项工作中，我们研究了哪些神经元组包含共同或冗余信息，该信息使用称为O信息的多元相互信息方法。我们观察到第一层是由冗余所主导的，暗示了一般共享特征（即检测边缘），而最后一层则以协同作用为主导，指示特定于本地类别的特定特征（即概念）。最后，我们表明O信息可用于多神经的重要性。这可以通过重新训练协同子网络来证明这一点，从而导致性能的最小变化。这些结果表明我们的方法可用于修剪和无监督的表示学习。
Quantifying which neurons are important with respect to the classification decision of a trained neural network is essential for understanding their inner workings. Previous work primarily attributed importance to individual neurons. In this work, we study which groups of neurons contain synergistic or redundant information using a multivariate mutual information method called the O-information. We observe the first layer is dominated by redundancy suggesting general shared features (i.e. detecting edges) while the last layer is dominated by synergy indicating local class-specific features (i.e. concepts). Finally, we show the O-information can be used for multi-neuron importance. This can be demonstrated by re-training a synergistic sub-network, which results in a minimal change in performance. These results suggest our method can be used for pruning and unsupervised representation learning.
