The ability to detect out-of-distribution (OOD) samples is vital to secure
the reliability of deep neural networks in real-world applications. Considering
the nature of OOD samples, detection methods should not have hyperparameters
that need to be tuned depending on incoming OOD samples. However, most of the
recently proposed methods do not meet this requirement, leading to compromised
performance in real-world applications. In this paper, we propose a simple,
hyperparameter-free method based on softmax of scaled cosine similarity. It
resembles the approach employed by modern metric learning methods, but it
differs in details; the differences are essential to achieve high detection
performance. We show through experiments that our method outperforms the
existing methods on the evaluation test recently proposed by Shafaei et al.,
which takes the above issue of hyperparameter dependency into account. We also
show that it achieves at least comparable performance to other methods on the
conventional test, where their hyperparameters are chosen using explicit OOD
samples. Furthermore, it is computationally more efficient than most of the
previous methods, since it needs only a single forward pass.

本文提出了一种基于缩放余弦相似性的 softmax 的简单、无超参数的方法，用于检测深度神经网络中的 out-of-distribution（分布外）样本。通过实验证明，该方法在考虑超参数依赖性的评估测试中优于现有方法，并且在传统测试中实现了至少与其他方法可比较的性能。此外，它的计算效率比大多数先前的方法更高，因为它只需要一个前向传递。