Out-of-distribution (OOD) detection methods have been developed to identify
objects that a model has not seen during training. The Outlier Exposure (OE)
methods use auxiliary datasets to train OOD detectors directly. However, the
collection and learning of representative OOD samples may pose challenges. To
tackle these issues, we propose the Outlier Aware Metric Learning (OAML)
framework. The main idea of our method is to use the k-NN algorithm and Stable
Diffusion model to generate outliers for training at the feature level without
making any distributional assumptions. To increase feature discrepancies in the
semantic space, we develop a mutual information-based contrastive learning
approach for learning from OOD data effectively. Both theoretical and empirical
results confirm the effectiveness of this contrastive learning technique.
Furthermore, we incorporate knowledge distillation into our learning framework
to prevent degradation of in-distribution classification accuracy. The
combination of contrastive learning and knowledge distillation algorithms
significantly enhances the performance of OOD detection. Experimental results
across various datasets show that our method significantly outperforms previous
OE methods.

我们提出了一种名为 Outlier Aware Metric Learning (OAML) 的框架，通过 k-NN 算法和稳定扩散模型在特征级别生成用于训练的异常样本，以提高语义空间中的特征差异，并结合基于互信息的对比学习方法，有效地利用 OOD 数据进行学习。此外，我们将知识蒸馏引入学习框架，以防止内部分类准确性的降低。通过对比学习和知识蒸馏算法的结合，我们的方法在各种数据集上显著优于以前的 OE 方法。

OAML：用于 OOD 检测增强的异常值感知度量学习

OAML: Outlier Aware Metric Learning for OOD Detection Enhancement

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）