Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
<|reference_start|>Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution: 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.<|reference_end|>
@article{liu2024rethinking,
    title={Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution},
    author={Kai Liu, Zhihang Fu, Sheng Jin, Chao Chen, Ze Chen, Rongxin Jiang, Fan
  Zhou, Yaowu Chen, Jieping Ye},
    journal={arXiv preprint arXiv:2407.16430},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16430},
    primaryClass={cs.CV}
}