In this contribution, we introduce a novel ensemble method for the
re-identification of industrial entities, using images of chipwood pallets and
galvanized metal plates as dataset examples. Our algorithms replace commonly
used, complex siamese neural networks with an ensemble of simplified,
rudimentary models, providing wider applicability, especially in
hardware-restricted scenarios. Each ensemble sub-model uses different types of
extracted features of the given data as its input, allowing for the creation of
effective ensembles in a fraction of the training duration needed for more
complex state-of-the-art models. We reach state-of-the-art performance at our
task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%,
and introduce five distinct feature extraction approaches, and study their
combination using different ensemble methods.

本文介绍了一种新的集成方法，用于工业实体的重新识别，使用了芯板托盘和镀锌金属板的图像作为数据集示例，并使用简化的、基本的模型代替常用的复杂孪生神经网络，提供更广泛的适用性，尤其适用于硬件限制的场景。我们的算法在训练时间的一小部分内使用了不同类型的数据特征作为输入，实现了创建有效集成模型的目标，并且在任务中达到了最先进的性能，排名 1 准确率超过 77%，排名 10 准确率超过 99%，并且介绍了五种不同的特征提取方法，并通过不同的集成方法对其组合进行了研究。

異構集成方法對重新識別的有效性研究

On the Effectiveness of Heterogeneous Ensemble Methods for  Re-identification

Recent work on interpretability in machine learning and AI has focused on the
building of simplified models that approximate the true criteria used to make
decisions. These models are a useful pedagogical device for teaching trained
professionals how to predict what decisions will be made by the complex system,
and most importantly how the system might break. However, when considering any
such model it's important to remember Box's maxim that "All models are wrong
but some are useful." We focus on the distinction between these models and
explanations in philosophy and sociology. These models can be understood as a
"do it yourself kit" for explanations, allowing a practitioner to directly
answer "what if questions" or generate contrastive explanations without
external assistance. Although a valuable ability, giving these models as
explanations appears more difficult than necessary, and other forms of
explanation may not have the same trade-offs. We contrast the different schools
of thought on what makes an explanation, and suggest that machine learning
might benefit from viewing the problem more broadly.

该论文关注机器学习中可解释性问题，侧重于简化模型的构建及不同形式的解释与说明，并探讨了机器学习在处理该问题时的广义视角。