ViLo-TM: Visual-Logic Mapping from Deep CNNs to Tsetlin Machines

Tsetlin Machines (TMs) provide explicit conjunctive rules and gradient-free adaptation, yet learning expressive visual features jointly with their discrete clauses remains difficult. We introduce ViLo-TM, a family of vision TMs that jointly learns CNN representations and visually traceable Boolean rules. Its learned spatial-evidence-to-literal (SE2L) interface maps pooled convolutional channel responses to ternary predicate states and paired literals, while continuous relaxations of literal inclusion and conjunction enable end-to-end optimization. Quantization-aware training (QAT) and export yield an integer CNN encoder and a Boolean TM in which each fired clause exposes its literal conditions and signed vote, linked to the source channel maps. The Boolean TM supports bitwise inference and multiplication-free learning through standard TM feedback (Type I/II) with the encoder and thresholds fixed. On CIFAR-10, the deployed Boolean TM exceeds the strongest compared vision-TM baseline (90.15% vs. 82.8%; initializer selected on test accuracy). On CUB-200-2011, Boolean conversion retains most of the QAT accuracy (75.92% $\to$ 75.01%, mean of three runs), and the deployed TM exactly matches the reference Boolean clause outputs and class scores on all test images (5,794).