We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a
novel Convolutional Neural Network (CNN) architecture that addresses, by
design, the open challenges of interpretability, formal verification, and logic
gate conversion. $\mathcal{TT}$net is built using CNNs' filters that are
equivalent to tractable truth tables and that we call Learning Truth Table
(LTT) blocks. The dual form of LTT blocks allows the truth tables to be easily
trained with gradient descent and makes these CNNs easy to interpret, verify
and infer. Specifically, $\mathcal{TT}$net is a deep CNN model that can be
automatically represented, after post-training transformation, as a sum of
Boolean decision trees, or as a sum of Disjunctive/Conjunctive Normal Form
(DNF/CNF) formulas, or as a compact Boolean logic circuit. We demonstrate the
effectiveness and scalability of $\mathcal{TT}$net on multiple datasets,
showing comparable interpretability to decision trees, fast complete/sound
formal verification, and scalable logic gate representation, all compared to
state-of-the-art methods. We believe this work represents a step towards making
CNNs more transparent and trustworthy for real-world critical applications.

本论文提出了一种名为 Truth-Table Net（TTNet）的卷积神经网络结构，该结构通过设计解决了可解释性、形式验证和逻辑门转换等难题，并在多个数据集上显示出与决策树可比的可解释性、快速完整合规验证和可扩展的逻辑门表示。