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）在工业流程的图像分类中应用广泛，这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，模型应对其自身预测的不确定性进行量化，以表明输出何时（不）应被信任。由多个独立神经网络构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，即快照集成、批量集成与多输入多输出集成。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量化性能。该研究首次提供了全面的比较分析，并提出了一种新颖的多样性质量指标，以单一度量量化集成模型在分布内与OOD数据集上的综合表现。研究结果凸显了批量集成作为深度集成的一种经济高效的竞争性替代方案。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、测试时间与内存存储方面实现了显著节约。