Image classification with neural networks (NNs) is widely used in industrial processes, situations where the model likely encounters unknown objects during deployment, i.e., out-of-distribution (OOD) data. Worryingly, NNs tend to make confident yet incorrect predictions when confronted with OOD data. To increase the models' reliability, they should quantify the uncertainty in their own predictions, communicating when the output should (not) be trusted. Deep ensembles, composed of multiple independent NNs, have been shown to perform strongly but are computationally expensive. Recent research has proposed more efficient NN ensembles, namely the snapshot, batch, and multi-input multi-output ensemble. This study investigates the predictive and uncertainty performance of efficient NN ensembles in the context of image classification for industrial processes. It is the first to provide a comprehensive comparison and it proposes a novel Diversity Quality metric to quantify the ensembles' performance on the in-distribution and OOD sets in one single metric. The results highlight the batch ensemble as a cost-effective and competitive alternative to the deep ensemble. It matches the deep ensemble in both uncertainty and accuracy while exhibiting considerable savings in training time, test time, and memory storage.

翻译：神经网络（NNs）在工业流程中的图像分类应用广泛，此类场景中模型在部署时很可能遇到未知物体，即分布外（OOD）数据。令人担忧的是，神经网络在面对OOD数据时倾向于做出置信度高但错误的预测。为提高模型的可靠性，模型应量化其自身预测的不确定性，以传达何时应（或不应）信任输出结果。由多个独立神经网络组成的深度集成方法已被证明性能优异，但计算成本高昂。近期研究提出了更高效的神经网络集成方法，包括快照集成、批量集成和多输入多输出集成。本研究探讨了高效神经网络集成在工业流程图像分类中的预测性能与不确定性表现。该研究首次提供了全面比较，并提出了一种新颖的多样性质量指标，以单一指标量化集成模型在分布内和OOD数据集上的综合性能。研究结果突显批量集成作为深度集成的高性价比替代方案，其在不确定性与准确性方面均与深度集成相当，同时在训练时间、测试时间和内存存储方面展现出显著优势。