通过多样化实现分布外检测（可证明）
Out-Of-Distribution Detection with Diversification (Provably)
AI总结：
本文针对OOD检测中辅助异常样本多样性不足导致泛化能力受限的问题，提出具有理论保证的diverseMix方法，通过高效增强训练数据的多样性，在大规模基准上取得了优越性能。
摘要：
分布外（OOD）检测对于确保机器学习模型的可靠部署至关重要。近期的进展集中在训练中利用易于获取的辅助异常样本（例如来自网络或其他数据集的数据）。然而，我们通过实验揭示，由于所收集的辅助异常样本多样性有限，这些方法在将其检测能力泛化到未知 OOD 数据时仍然存在困难。因此，我们从泛化的角度深入研究了这一问题，并证明更多样化的辅助异常样本集对于增强检测能力是必不可少的。然而，在实践中，收集足够多样化的辅助异常样本数据既困难又昂贵。因此，我们提出了一种简单实用且具有理论保证的方法，称为面向 OOD 检测的多样性诱导 Mixup（diverseMix），该方法以高效的方式增强了用于训练的辅助异常样本集的多样性。大量实验表明，diverseMix 在常用以及近期具有挑战性的大规模基准上取得了优越的性能，这进一步证实了辅助异常样本多样性的重要性。
英文摘要：
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers (e.g., data from the web or other datasets) in training. However, we experimentally reveal that these methods still struggle to generalize their detection capabilities to unknown OOD data, due to the limited diversity of the auxiliary outliers collected. Therefore, we thoroughly examine this problem from the generalization perspective and demonstrate that a more diverse set of auxiliary outliers is essential for enhancing the detection capabilities. However, in practice, it is difficult and costly to collect sufficiently diverse auxiliary outlier data. Therefore, we propose a simple yet practical approach with a theoretical guarantee, termed Diversity-induced Mixup for OOD detection (diverseMix), which enhances the diversity of auxiliary outlier set for training in an efficient way. Extensive experiments show that diverseMix achieves superior performance on commonly used and recent challenging large-scale benchmarks, which further confirm the importance of the diversity of auxiliary outliers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
