  Two prevalent types of distributional shifts in machine learning are the
covariate shift (as observed across different domains) and the semantic shift
(as seen across different classes). Traditional OOD detection techniques
typically address only one of these shifts. However, real-world testing
environments often present a combination of both covariate and semantic shifts.
In this study, we introduce a novel problem, semantic OOD detection across
domains, which simultaneously addresses both distributional shifts. To this
end, we introduce two regularization strategies: domain generalization
regularization, which ensures semantic invariance across domains to counteract
the covariate shift, and OOD detection regularization, designed to enhance OOD
detection capabilities against the semantic shift through energy bounding.
Through rigorous testing on three standard domain generalization benchmarks,
our proposed framework showcases its superiority over conventional domain
generalization approaches in terms of OOD detection performance. Moreover, it
holds its ground by maintaining comparable InD classification accuracy.

Artificial Intelligence