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.

采用先进的训练策略，利用辅助离群数据训练机器学习模型以改善在真实世界中的离群检测性能。通过引入 Hopfield Boosting 算法，利用现代 Hopfield energy (MHE) 技术加强了模型在内部分布数据和辅助离群数据之间的决策边界，从而实现更精确的离群检测，使 FPR95 指标从 2.28 降低到 0.92（在 CIFAR-10 上）和从 11.76 降低到 7.94（在 CIFAR-100 上）