Detecting out-of-distribution (OOD) data is critical to building reliable machine learning systems in the open world. Among the existing OOD detection methods, ReAct is famous for its simplicity and efficiency, and has good theoretical analysis. The gap between ID data and OOD data is enlarged by clipping the larger activation value. But the question is, is this operation optimal? Is there a better way to expand the spacing between ID samples and OOD samples in theory? Driven by these questions, we propose the Variational Recified Acitvations method. To verify the effectiveness of our method, we conduct experiments on many benchmark datasets. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches. Meanwhile, our method is easy to implement and does not require additional OOD data or fine-tuning process. We can realize OOD detection in only one forward pass.

翻译：检测分布外（OOD）数据对于构建开放世界中的可靠机器学习系统至关重要。在现有的OOD检测方法中，ReAct以其简洁性和高效性而闻名，并具备良好的理论分析。通过裁剪较大的激活值，可以扩大ID数据与OOD数据之间的差距。但问题在于，这一操作是否最优？是否存在理论更优的方法来扩大ID样本与OOD样本之间的间隔？受这些问题驱动，我们提出了变分整流激活方法。为验证该方法的有效性，我们在多个基准数据集上开展了实验。实验结果表明，我们的方法优于现有最先进的方法。同时，该方法易于实现，无需额外的OOD数据或微调过程，仅需一次前向传播即可完成OOD检测。