标题： 在可解释设计卷积神经网络中学习局部离散特征 显示英文标题

标题： Learning local discrete features in explainable-by-design convolutional neural networks

摘要： 我们提出的框架试图通过引入一种基于侧向抑制机制的可解释设计卷积神经网络（CNN）来打破性能和可解释性之间的权衡。 ExplaiNet模型由预测器组成，即一个具有残差或密集跳跃连接的高精度CNN，以及一个表达网络神经元空间相互作用的概率图。 每个图节点的值是一个局部离散特征（LDF）向量，这是一种补丁描述符，表示按激活强度排序的对抗神经元的索引，这些索引是通过梯度下降学习得到的。 使用LDF作为序列，我们可以通过重新利用EXTREME，一种通常用于分子生物学的基于EM的序列基序发现方法，来增加解释的简洁性。 对于每个中间图像表示，拥有一个离散特征基序矩阵，而不是连续激活张量，使我们能够利用贝叶斯网络固有的可解释性。 通过收集观察结果并直接计算概率，我们可以解释相邻层次基序之间的因果关系，并将模型的输出归因于全局基序。 此外，在各种微小图像基准数据集上的实验确认，我们的预测器在给定参数和/或层的数量下可以确保与基线架构相同水平的性能。 我们的新方法显示出超越此性能的潜力，同时提供额外的解释流。 在解决的MNIST分类任务中，它达到了与最先进单模型相当的性能，使用标准训练设置和0.75百万参数。 显示英文摘要

摘要： Our proposed framework attempts to break the trade-off between performance and explainability by introducing an explainable-by-design convolutional neural network (CNN) based on the lateral inhibition mechanism. The ExplaiNet model consists of the predictor, that is a high-accuracy CNN with residual or dense skip connections, and the explainer probabilistic graph that expresses the spatial interactions of the network neurons. The value on each graph node is a local discrete feature (LDF) vector, a patch descriptor that represents the indices of antagonistic neurons ordered by the strength of their activations, which are learned with gradient descent. Using LDFs as sequences we can increase the conciseness of explanations by repurposing EXTREME, an EM-based sequence motif discovery method that is typically used in molecular biology. Having a discrete feature motif matrix for each one of intermediate image representations, instead of a continuous activation tensor, allows us to leverage the inherent explainability of Bayesian networks. By collecting observations and directly calculating probabilities, we can explain causal relationships between motifs of adjacent levels and attribute the model's output to global motifs. Moreover, experiments on various tiny image benchmark datasets confirm that our predictor ensures the same level of performance as the baseline architecture for a given count of parameters and/or layers. Our novel method shows promise to exceed this performance while providing an additional stream of explanations. In the solved MNIST classification task, it reaches a comparable to the state-of-the-art performance for single models, using standard training setup and 0.75 million parameters.