Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to concentrate on hard-to-distinguish auxiliary outlier examples that lie close to the decision boundary between in-distribution and auxiliary outlier data. Our method achieves a new state-of-the-art in OOD detection with outlier exposure, improving the FPR95 metric from 2.28 to 0.92 on CIFAR-10 and from 11.76 to 7.94 on CIFAR-100.

翻译：分布外（OOD）检测在现实世界部署机器学习模型时至关重要。与未采用高级训练策略的方法相比，在训练过程中引入辅助异常数据的异常暴露方法能够显著提升OOD检测性能。我们提出了一种提升方法——Hopfield提升，该方法利用现代Hopfield能量（MHE）来锐化分布内数据与OOD数据之间的决策边界。Hopfield提升鼓励模型聚焦于靠近分布内与辅助异常数据决策边界的难以区分的辅助异常样本。我们的方法在基于异常暴露的OOD检测中达到了新最优性能，将CIFAR-10数据集上的FPR95指标从2.28改善至0.92，CIFAR-100数据集上从11.76改善至7.94。