Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation method for image classifiers based on the shearlet transform -- a multiscale directional image representation. Current mask explanation methods are regularized by smoothness constraints that protect against undesirable fine-grained explanation artifacts. However, the smoothness of a mask limits its ability to separate fine-detail patterns, that are relevant for the classifier, from nearby nuisance patterns, that do not affect the classifier. ShearletX solves this problem by avoiding smoothness regularization all together, replacing it by shearlet sparsity constraints. The resulting explanations consist of a few edges, textures, and smooth parts of the original image, that are the most relevant for the decision of the classifier. To support our method, we propose a mathematical definition for explanation artifacts and an information theoretic score to evaluate the quality of mask explanations. We demonstrate the superiority of ShearletX over previous mask based explanation methods using these new metrics, and present exemplary situations where separating fine-detail patterns allows explaining phenomena that were not explainable before.

翻译：已知图像分类器难以解释，因此需要解释方法来理解其决策。我们提出ShearletX，一种基于剪切波变换（一种多尺度方向图像表示）的新型掩码解释方法。现有的掩码解释方法受平滑性约束限制，以防止产生不良的细粒度解释伪影。然而，掩码的平滑性会限制其从附近不影响分类器的干扰模式中分离出对分类器相关的精细细节模式的能力。ShearletX通过完全避免平滑性正则化，并用剪切波稀疏约束替代它来解决这一问题。由此得到的解释由原始图像中与分类器决策最相关的一些边缘、纹理和平滑部分组成。为了支持我们的方法，我们提出了解释伪影的数学定义，以及一个信息论评分来评估掩码解释的质量。我们使用这些新指标证明了ShearletX优于以往的基于掩码的解释方法，并展示了在分离精细细节模式使得解释以前无法解释的现象方面的示例性情况。