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The Convolutional Tsetlin Machine

The Convolutional Tsetlin Machine peaks at 99.51% accuracy on MNIST

The Convolutional Tsetlin Machine learns interpretable filters using propositional formulae. It is an interpretable alternative to convolutional neural networks that provides competitive accuracy in several benchmarks. 

Deep neural networks have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of interpretability. The recent Tsetlin Machine (TM) attempts to address this lack by using easy-to-interpret conjunctive clauses in propositional logic to solve complex pattern recognition problems. The TM provides competitive accuracy in several benchmarks while keeping the important property of interpretability. It further facilitates hardware-near implementation since inputs, patterns, and outputs are expressed as bits, while recognition and learning rely on straightforward bit manipulation. The Convolutional Tsetlin Machine (CTM) exploits the TM paradigm, providing an interpretable alternative to convolutional neural networks (CNNs). Whereas the TM categorizes an image by employing each clause once to the whole image, the CTM uses each clause as a convolution filter. That is, a clause is evaluated multiple times, once per image patch taking part in the convolution. To make the clauses location-aware, each patch is further augmented with its coordinates within the image. The output of a convolution clause is obtained simply by ORing the outcome of evaluating the clause on each patch. In the learning phase of the TM, clauses that evaluate to 1 are contrasted against the input. For the CTM, we instead contrast against one of the patches, randomly selected among the patches that made the clause evaluate to 1. Accordingly, the standard Type I and Type II feedback of the classic TM can be employed directly, without further modification. The CTM obtains a peak test accuracy of 99.51% on MNIST, 96.21% on Kuzushiji-MNIST, 89.56% on Fashion-MNIST, and 100% on the 2D Noisy XOR Problem, which is competitive with results reported for simple 4-layer CNNs, BinaryConnect, and a recent FPGA-accelerated Binary CNN.

Published: Friday 24 May 2019

 

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