Ensembles of neural networks achieve superior performance compared to stand-alone networks not only in terms of accuracy on in-distribution data but also on data with distributional shift alongside improved uncertainty calibration. Diversity among networks in an ensemble is believed to be key for building strong ensembles, but typical approaches only ensemble different weight vectors of a fixed architecture. Instead, we investigate neural architecture search (NAS) for explicitly constructing ensembles to exploit diversity among networks of varying architectures and to achieve robustness against distributional shift. By directly optimizing ensemble performance, our methods implicitly encourage diversity among networks, without the need to explicitly define diversity. We find that the resulting ensembles are more diverse compared to ensembles composed of a fixed architecture and are therefore also more powerful. We show significant improvements in ensemble performance on image classification tasks both for in-distribution data and during distributional shift with better uncertainty calibration.

本文提出两种构建神经网络集合的方法，使用不同的体系结构，以细化预测结果并利用架构变化作为集合的差异性的来源，并在多个分类任务和现代架构搜索空间方面表现优于深度集成。

神经集成搜索用于不确定性估计和数据集偏移