The Tsetlin Machine (TM) has achieved competitive results on several image
classification benchmarks, including MNIST, K-MNIST, F-MNIST, and CIFAR-2.
However, color image classification is arguably still in its infancy for TMs,
with CIFAR-10 being a focal point for tracking progress. Over the past few
years, TM's CIFAR-10 accuracy has increased from around 61% in 2020 to 75.1% in
2023 with the introduction of Drop Clause. In this paper, we leverage the
recently proposed TM Composites architecture and introduce a range of TM
Specialists that use various image processing techniques. These include Canny
edge detection, Histogram of Oriented Gradients, adaptive mean thresholding,
adaptive Gaussian thresholding, Otsu's thresholding, color thermometers, and
adaptive color thermometers. In addition, we conduct a rigorous hyperparameter
search, where we uncover optimal hyperparameters for several of the TM
Specialists. The result is a toolbox that provides new state-of-the-art results
on CIFAR-10 for TMs with an accuracy of 82.8%. In conclusion, our toolbox of TM
Specialists forms a foundation for new TM applications and a landmark for
further research on TM Composites in image analysis.

本文通过引入 TM Composites 架构和一系列的 TM Specialists，利用各种图像处理技术，进行了 CIFAR-10 的图像分类任务，达到了 82.8% 的准确率，为 Tsetlin Machine 应用和进一步的 TM Composites 图像分析研究奠定了基础。

具有 Tsetlin 机组合的高级图像处理优化工具箱

An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine  Composites

Tsetlin Machines (TMs) provide a fundamental shift from arithmetic-based to
logic-based machine learning. Supporting convolution, they deal successfully
with image classification datasets like MNIST, Fashion-MNIST, and CIFAR-2.
However, the TM struggles with getting state-of-the-art performance on CIFAR-10
and CIFAR-100, representing more complex tasks. This paper introduces
plug-and-play collaboration between specialized TMs, referred to as TM
Composites. The collaboration relies on a TM's ability to specialize during
learning and to assess its competence during inference. When teaming up, the
most confident TMs make the decisions, relieving the uncertain ones. In this
manner, a TM Composite becomes more competent than its members, benefiting from
their specializations. The collaboration is plug-and-play in that members can
be combined in any way, at any time, without fine-tuning. We implement three TM
specializations in our empirical evaluation: Histogram of Gradients, Adaptive
Gaussian Thresholding, and Color Thermometers. The resulting TM Composite
increases accuracy on Fashion-MNIST by two percentage points, CIFAR-10 by
twelve points, and CIFAR-100 by nine points, yielding new state-of-the-art
results for TMs. Overall, we envision that TM Composites will enable an
ultra-low energy and transparent alternative to state-of-the-art deep learning
on more tasks and datasets.

Tsetlin Machines (TMs) 的组合（TM Composites）利用 TMs 的特化和判断能力，在团队合作中通过选择最自信的 TMs 来取得更优越的性能，提高了图像分类任务的准确性，并成为具有新的最先进结果的方法。