Deep neural networks have attained remarkable performance when applied to
data that comes from the same distribution as that of the training set, but can
significantly degrade otherwise. Therefore, detecting whether an example is
out-of-distribution (OoD) is crucial to enable a system that can reject such
samples or alert users. Recent works have made significant progress on OoD
benchmarks consisting of small image datasets. However, many recent methods
based on neural networks rely on training or tuning with both in-distribution
and out-of-distribution data. The latter is generally hard to define a-priori,
and its selection can easily bias the learning. We base our work on a popular
method ODIN, proposing two strategies for freeing it from the needs of tuning
with OoD data, while improving its OoD detection performance. We specifically
propose to decompose confidence scoring as well as a modified input
pre-processing method. We show that both of these significantly help in
detection performance. Our further analysis on a larger scale image dataset
shows that the two types of distribution shifts, specifically semantic shift
and non-semantic shift, present a significant difference in the difficulty of
the problem, providing an analysis of when ODIN-like strategies do or do not
work.

本篇论文提出了两种方法，用于加强 ODIN 检测方法在不需经过 out-of-distribution 数据训练或调整的情况下的性能表现，这两种方法包括对置信度打分的分解以及对输入数据的改进预处理。文章还对两种分布偏移（语义偏移和非语义偏移）的问题上，展开了深入细致的分析。

通用 ODIN: 无需在外部数据集中学习即可检测出分布外图像

Generalized ODIN: Detecting Out-of-distribution Image without Learning  from Out-of-distribution Data

We propose a novel confidence scoring mechanism for deep neural networks
based on a two-model paradigm involving a base model and a meta-model. The
confidence score is learned by the meta-model observing the base model
succeeding/failing at its task. As features to the meta-model, we investigate
linear classifier probes inserted between the various layers of the base model.
Our experiments demonstrate that this approach outperforms various baselines in
a filtering task, i.e., task of rejecting samples with low confidence.
Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with
and without added noise. We discuss the importance of confidence scoring to
bridge the gap between experimental and real-world applications.

本文提出了一种基于两个模型范式的深度神经网络置信度评分机制，通过在任务中观察基础模型的成功 / 失败来学习元模型的置信度评分，实验结果表明该方法在过滤任务中优于各种基线，并使用带或不带噪声的 CIFAR-10 和 CIFAR-100 数据集进行了实验研究，讨论了置信度评分在实验和实际应用之间的重要性。