It is known that neural networks have the problem of being over-confident
when directly using the output label distribution to generate uncertainty
measures. Existing methods mainly resolve this issue by retraining the entire
model to impose the uncertainty quantification capability so that the learned
model can achieve desired performance in accuracy and uncertainty prediction
simultaneously. However, training the model from scratch is computationally
expensive and may not be feasible in many situations. In this work, we consider
a more practical post-hoc uncertainty learning setting, where a well-trained
base model is given, and we focus on the uncertainty quantification task at the
second stage of training. We propose a novel Bayesian meta-model to augment
pre-trained models with better uncertainty quantification abilities, which is
effective and computationally efficient. Our proposed method requires no
additional training data and is flexible enough to quantify different
uncertainties and easily adapt to different application settings, including
out-of-domain data detection, misclassification detection, and trustworthy
transfer learning. We demonstrate our proposed meta-model approach's
flexibility and superior empirical performance on these applications over
multiple representative image classification benchmarks.

提出了一种基于贝叶斯元模型的方法，该方法用于增强预训练模型的不确定性量化能力，以实现不同应用场景下的预测性能，例如图像分类中的领域外数据检测、错分检测和可信迁移学习，无需额外的训练数据，从而在多个代表性图像分类基准测试上展示了更好的表现。