将神经网络转化为可解释的分段线性表示，揭示其内部工作机制。
Regional, Lattice and Logical Representations of Neural Networks
- 通过算法将带ReLU的前馈网络转为分段线性区域表示
- 发现网络规模越大，区域表示复杂度越高
- 可进一步生成格与逻辑表示，适合可解释性研究者
神经网络可解释性的可能路径是将其近似表示为分段线性函数形式，其中输入空间被划分为若干区域，每个区域对应一个计算输出的线性函数。本文提出一种算法，用于将隐藏层使用ReLU激活函数、输出层使用截断恒等激活函数的前馈神经网络转换为这种区域表示。我们还对不同规模神经网络在该方法下的区域表示复杂度进行了实证分析。只要满足特定性质，区域表示可直接导出格表示与逻辑表示。因此，我们进一步实证研究了该算法生成的表示满足该性质的程度。
原文摘要 · Abstract (English)
A possible path to the interpretability of neural networks is to (approximately) represent them in the regional format of piecewise linear functions, where regions of inputs are associated to linear functions computing the network outputs. We present an algorithm for the translation of feedforward neural networks with ReLU activation functions in hidden layers and truncated identity activation functions in the output layer. We also empirically investigate the complexity of regional representations outputted by our method for neural networks with varying sizes. Lattice and logical representations of neural networks are straightforward from regional representations as long as they satisfy a specific property. So we empirically investigate to what extent the translations by our algorithm satisfy such property.
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