In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. However, a lack of strong guarantees on their behaviour have raised concerns over their use in safety-critical applications. A first step to understanding these networks is to develop alternate representations that allow for further analysis. It has been shown that neural networks with piecewise affine activation functions are themselves piecewise affine, with their domains consisting of a vast number of linear regions. So far, the research on this topic has focused on counting the number of linear regions, rather than obtaining explicit piecewise affine representations. This work presents a novel algorithm that can compute the piecewise affine form of any fully connected neural network with rectified linear unit activations.

中文翻译：

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剖析深度神经网络

作为大量数据和处理能力的交换，深度神经网络产生了在许多领域提供最先进预测能力的模型。然而，由于缺乏对其行为的有力保证，这引起了人们对其在安全关键应用程序中使用的担忧。理解这些网络的第一步是开发允许进一步分析的替代表示。已经表明，具有分段仿射激活函数的神经网络本身是分段仿射的，其域由大量线性区域组成。到目前为止，关于这个主题的研究主要集中在计算线性区域的数量，而不是获得明确的分段仿射表示。