  Out-of-distribution (OOD) detection is a crucial aspect of deploying machine
learning models in open-world applications. Empirical evidence suggests that
training with auxiliary outliers substantially improves OOD detection. However,
such outliers typically exhibit a distribution gap compared to the test OOD
data and do not cover all possible test OOD scenarios. Additionally,
incorporating these outliers introduces additional training burdens. In this
paper, we introduce a novel paradigm called test-time OOD detection, which
utilizes unlabeled online data directly at test time to improve OOD detection
performance. While this paradigm is efficient, it also presents challenges such
as catastrophic forgetting. To address these challenges, we propose adaptive
outlier optimization (AUTO), which consists of an in-out-aware filter, an ID
memory bank, and a semantically-consistent objective. AUTO adaptively mines
pseudo-ID and pseudo-OOD samples from test data, utilizing them to optimize
networks in real time during inference. Extensive results on CIFAR-10,
CIFAR-100, and ImageNet benchmarks demonstrate that AUTO significantly enhances
OOD detection performance.
