We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. As a result, CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly, CoDA Nets can be combined with conventional neural network models to yield powerful classifiers that more easily scale to complex datasets such as Imagenet whilst exhibiting an increased interpretable depth, i.e., the output can be explained well in terms of contributions from intermediate layers within the network.

翻译：我们提出了一种名为卷积动态对齐网络（CoDA Nets）的新型神经网络模型系列，这些模型作为高性能分类器，具备高度的固有可解释性。其核心构建模块是动态对齐单元（DAU），通过优化使其能够利用动态计算的权重向量变换输入，这些权重向量与任务相关模式对齐。因此，CoDA Nets通过一系列依赖于输入的线性变换建模分类预测，从而将输出线性分解为各输入贡献。由于DAU的对齐特性，生成的贡献图与判别性输入模式保持一致。这些模型固有的分解具有高视觉质量，并在定量指标上优于现有的归因方法。此外，CoDA Nets构成了高性能分类器，在CIFAR-10和TinyImagenet等数据集上取得了与ResNet和VGG模型相当的结果。最后，CoDA Nets可与传统神经网络模型结合，形成强大的分类器，更易于扩展到ImageNet等复杂数据集，同时展现出增强的可解释深度，即输出可以基于网络中间层的贡献得到充分解释。