Explanations in Computer Vision are often desired, but most Deep Neural Networks can only provide saliency maps with questionable faithfulness. Self-Explaining Neural Networks (SENN) extract interpretable concepts with fidelity, diversity, and grounding to combine them linearly for decision-making. While they can explain what was recognized, initial realizations lack accuracy and general applicability. We propose the Quantized-Self-Explaining Neural Network Q-SENN. Q-SENN satisfies or exceeds the desiderata of SENN while being applicable to more complex datasets and maintaining most or all of the accuracy of an uninterpretable baseline model, out-performing previous work in all considered metrics. Q-SENN describes the relationship between every class and feature as either positive, negative or neutral instead of an arbitrary number of possible relations, enforcing more binary human-friendly features. Since every class is assigned just 5 interpretable features on average, Q-SENN shows convincing local and global interpretability. Additionally, we propose a feature alignment method, capable of aligning learned features with human language-based concepts without additional supervision. Thus, what is learned can be more easily verbalized. The code is published: https://github.com/ThomasNorr/Q-SENN

计算机视觉中的解释通常是需要的，但大多数深度神经网络只能提供有问题的准确性的显著性图。自说明神经网络（SENN）提取可解释的概念，并将它们线性组合用于决策。我们提出了量化自说明神经网络Q-SENN，它能够满足或超越SENN的期望，并适用于更复杂的数据集，并保持不可解释基准模型的准确性，以所有考虑的指标来看，优于以往的工作。Q-SENN将每个类别与特征之间的关系描述为正、负或中性，而不是一些任意可能的关系，从而实现了更二元的人类友好特征。由于每个类别平均只分配了5个可解释特征，Q-SENN具有令人信服的局部和全局可解释性。此外，我们提出了一种特征对齐方法，能够在无需额外监督的情况下将学到的特征与基于人类语言的概念对齐。因此，所学到的内容可以更容易地用语言表达出来。

Q-SENN：量化自解释神经网络