论文标题
通过辅助模型估算置信度
Confidence Estimation via Auxiliary Models
论文作者
论文摘要
可靠地量化深神经分类器的信心是在安全至关重要的应用中部署此类模型的具有挑战性但根本的要求。在本文中，我们引入了一个新颖的目标标准，以实现模型置信度，即真实类概率（TCP）。我们表明，与标准最大类概率（MCP）相比，TCP为置信度提供了更好的特性。由于真实的类是在测试时间未知的本质上，因此我们建议通过辅助模型从数据中学习TCP标准，并引入适合这种情况的特定学习方案。我们评估了对失败预测的任务和自我训练的伪标记，以进行领域适应，这两者都需要有效的置信度估计。进行了广泛的实验，以验证每个任务中提出的方法的相关性。我们研究各种网络体系结构，并使用小型和大型数据集进行图像分类和语义分割的实验。在每个经过测试的基准测试中，我们的方法的表现都超过了强大的基准。
Reliably quantifying the confidence of deep neural classifiers is a challenging yet fundamental requirement for deploying such models in safety-critical applications. In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP). We show that TCP offers better properties for confidence estimation than standard maximum class probability (MCP). Since the true class is by essence unknown at test time, we propose to learn TCP criterion from data with an auxiliary model, introducing a specific learning scheme adapted to this context. We evaluate our approach on the task of failure prediction and of self-training with pseudo-labels for domain adaptation, which both necessitate effective confidence estimates. Extensive experiments are conducted for validating the relevance of the proposed approach in each task. We study various network architectures and experiment with small and large datasets for image classification and semantic segmentation. In every tested benchmark, our approach outperforms strong baselines.
