本文介绍了置信度优化（CO）分数，以直接测量热插拔/显着图的贡献到模型的分类性能。可说明的人工智能（XAI）社区中使用的常见热映射生成方法通过我们称之为增强解释（AX）来测试。我们在这些热爱方法的CO分配中找到了一个惊人的\ Texit {Gap}。间隙可能用作深度神经网络（DNN）预测的正确性的新颖指标。我们进一步介绍了生成的AX（GAX）方法以产生能够获得高CO分数的显着图。使用迷人，我们也定性展示了DNN架构的不行性。

深度加强学习中的透明度和公平问题可能源于用于学习其政策，价值功能等的深度神经网络的黑匣子性质。本文提出了一种通过神经网络的自下而上设计来规避问题的方法（NN）具有详细的解释性，其中每个神经元或层具有自己的含义和实用程序，与人类理解的概念相对应。通过刻意设计，我们表明Lavaland可能使用NN模型解决了一些参数。此外，我们介绍了由反向奖励设计的自奖励设计（SRD），因此我们的可解释设计可以（1）通过纯设计解决问题（虽然不完美）（2）通过SRD（3）进行优化（3）进行避免通过识别在\（W_ {Unknown} \）中的激活聚合的神经元的灭活来识别神经元的灭活。

本文提出了用于通用近似的两个自下而上的可解释的神经网络（NN）构造，即三角形构造的NN（TNN）和半量化激活NN（SQANN）。值得注意的属性是（1）对灾难性的遗忘（2）对训练数据集（3）对输入\（X \）的任意高精度的证明存在的存在，用户可以识别其激活“指纹”的培训数据的特定样本。类似于\（x \）的激活。用户还可以识别出不分发的样本。

Post-hoc analysis is a popular category in eXplainable artificial intelligence (XAI) study. In particular, methods that generate heatmaps have been used to explain the deep neural network (DNN), a black-box model. Heatmaps can be appealing due to the intuitive and visual ways to understand them but assessing their qualities might not be straightforward. Different ways to assess heatmaps' quality have their own merits and shortcomings. This paper introduces a synthetic dataset that can be generated adhoc along with the ground-truth heatmaps for more objective quantitative assessment. Each sample data is an image of a cell with easily recognized features that are distinguished from localization ground-truth mask, hence facilitating a more transparent assessment of different XAI methods. Comparison and recommendations are made, shortcomings are clarified along with suggestions for future research directions to handle the finer details of select post-hoc analysis methods. Furthermore, mabCAM is introduced as the heatmap generation method compatible with our ground-truth heatmaps. The framework is easily generalizable and uses only standard deep learning components.