Annotating the right data for training deep neural networks is an important
challenge. Active learning using uncertainty estimates from Bayesian Neural
Networks (BNNs) could provide an effective solution to this. Despite being
theoretically principled, BNNs require approximations to be applied to
large-scale problems, where both performance and uncertainty estimation are
crucial. In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a
scalable technique that uses a regularized ensemble to approximate a deep BNN.
We conduct a series of large-scale visual active learning experiments to
evaluate DPEs on classification with the CIFAR-10, CIFAR-100 and ImageNet
datasets, and semantic segmentation with the BDD100k dataset. Our models
require significantly less training data to achieve competitive performances,
and steadily improve upon strong active learning baselines as the annotation
budget is increased.

本论文介绍了一种基于 Deep Probabilistic Ensembles 的可扩展方法，该方法使用一个规则化的集合来近似 deep BNN。我们对大规模的视觉主动学习实验进行了一系列研究并在 CIFAR-10，CIFAR-100 和 ImageNet 数据集，以及 BDD100k 数据集上进行了语义分割的评估。我们的模型需要更少的训练数据才能实现竞争性能，并随着注释预算的增加而稳步提高。