Existing out-of-distribution (OOD) detection literature clearly defines
semantic shift as a sign of OOD but does not have a consensus over covariate
shift. Samples experiencing covariate shift but not semantic shift are either
excluded from the test set or treated as OOD, which contradicts the primary
goal in machine learning -- being able to generalize beyond the training
distribution. In this paper, we take into account both shift types and
introduce full-spectrum OOD (FS-OOD) detection, a more realistic problem
setting that considers both detecting semantic shift and being tolerant to
covariate shift; and designs three benchmarks. These new benchmarks have a more
fine-grained categorization of distributions (i.e., training ID,
covariate-shifted ID, near-OOD, and far-OOD) for the purpose of more
comprehensively evaluating the pros and cons of algorithms. To address the
FS-OOD detection problem, we propose SEM, a simple feature-based semantics
score function. SEM is mainly composed of two probability measures: one is
based on high-level features containing both semantic and non-semantic
information, while the other is based on low-level feature statistics only
capturing non-semantic image styles. With a simple combination, the
non-semantic part is cancelled out, which leaves only semantic information in
SEM that can better handle FS-OOD detection. Extensive experiments on the three
new benchmarks show that SEM significantly outperforms current state-of-the-art
methods. Our code and benchmarks are released in
this https URL.

本文提出了一种全光谱 OOD 检测的新问题，并设计了三个基准以测试算法性能。针对这个问题，我们提出了一个简单的面向特征的 SEM 方法来检测语义转移，可以更好地处理 FS-OOD 检测，并且 SEM 明显优于当前最先进的方法。