@inproceedings{
liu2024rethinking,
title={Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution},
author={Kai Liu and Zhihang Fu and Sheng Jin and Chao Chen and Ze Chen and Rongxin Jiang and Fan Zhou and Yaowu Chen and Jieping Ye},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=EWxNEnFjKR}
}
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 ap- proaches. Code is available at https://github.com/alibaba/imood.
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
Kai Liu Zhihang Fu Sheng Jin Chao Chen Ze Chen Rongxin Jiang Fan Zhou Yaowu Chen Jieping Ye