  Detecting and rejecting unknown out-of-distribution (OOD) samples is critical
for deployed neural networks to void unreliable predictions. In real-world
scenarios, however, the efficacy of existing OOD detection methods is often
impeded by the inherent imbalance of in-distribution (ID) data, which causes
significant performance decline. Through statistical observations, we have
identified two common challenges faced by different OOD detectors:
misidentifying tail class ID samples as OOD, while erroneously predicting OOD
samples as head class from ID. To explain this phenomenon, we introduce a
generalized statistical framework, termed ImOOD, to formulate the OOD detection
problem on imbalanced data distribution. Consequently, the theoretical analysis
reveals that there exists a class-aware bias item between balanced and
imbalanced OOD detection, which contributes to the performance gap. Building
upon this finding, we present a unified training-time regularization technique
to mitigate the bias and boost imbalanced OOD detectors across architecture
designs. Our theoretically grounded method translates into consistent
improvements on the representative CIFAR10-LT, CIFAR100-LT, and ImageNet-LT
benchmarks against several state-of-the-art OOD detection approaches. Code will
be made public soon.
