Recent research underscores the pivotal role of the Out-of-Distribution (OOD)
feature representation field scale in determining the efficacy of models in OOD
detection. Consequently, the adoption of model ensembles has emerged as a
prominent strategy to augment this feature representation field, capitalizing
on anticipated model diversity.
However, our introduction of novel qualitative and quantitative model
ensemble evaluation methods, specifically Loss Basin/Barrier Visualization and
the Self-Coupling Index, reveals a critical drawback in existing ensemble
methods. We find that these methods incorporate weights that are
affine-transformable, exhibiting limited variability and thus failing to
achieve the desired diversity in feature representation.
To address this limitation, we elevate the dimensions of traditional model
ensembles, incorporating various factors such as different weight
initializations, data holdout, etc., into distinct supervision tasks. This
innovative approach, termed Multi-Comprehension (MC) Ensemble, leverages
diverse training tasks to generate distinct comprehensions of the data and
labels, thereby extending the feature representation field.
Our experimental results demonstrate the superior performance of the MC
Ensemble strategy in OOD detection compared to both the naive Deep Ensemble
method and a standalone model of comparable size. This underscores the
effectiveness of our proposed approach in enhancing the model's capability to
detect instances outside its training distribution.

通过创新的方法将不同的监督任务纳入传统的模型集成，构建了多理解（MC）集成机制，通过生成数据和标签的不同理解来拓展特征表达领域，实验证明了该方法相对于其他方法在 OOD 检测中的卓越性能。

基于深度多理解集成的异常检测

Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble

Determining whether inputs are out-of-distribution (OOD) is an essential
building block for safely deploying machine learning models in the open world.
However, previous methods relying on the softmax confidence score suffer from
overconfident posterior distributions for OOD data. We propose a unified
framework for OOD detection that uses an energy score. We show that energy
scores better distinguish in- and out-of-distribution samples than the
traditional approach using the softmax scores. Unlike softmax confidence
scores, energy scores are theoretically aligned with the probability density of
the inputs and are less susceptible to the overconfidence issue. Within this
framework, energy can be flexibly used as a scoring function for any
pre-trained neural classifier as well as a trainable cost function to shape the
energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained
WideResNet, using the energy score reduces the average FPR (at TPR 95%) by
18.03% compared to the softmax confidence score. With energy-based training,
our method outperforms the state-of-the-art on common benchmarks.

在开放世界中安全部署机器学习模型的重要基础是确定输入是否为 ODD, 然而，传统基于 softmax 置信度得分的方法在 ODD 数据的后验分布上存在过度自信的问题，本文提出了一个使用能量分数的 ODD 检测的统一框架，理论上更能够识别 “内部” 和 “外部” 数据。这个框架内可以灵活地将能量用作得分函数和 OOR 检测的可训练成本函数，与 CIFAR-10 预训练的 WideResNet 相比，使用能量分数将平均 FPR （在 TPR 95％处）降低了 18.03％，并且使用基于能量的训练，我们的方法在常见基准上表现优异。

基于能量的区分度检测

Energy-based Out-of-distribution Detection

The Mahalanobis distance-based confidence score, a recently proposed anomaly
detection method for pre-trained neural classifiers, achieves state-of-the-art
performance on both out-of-distribution (OoD) and adversarial examples
detection. This work analyzes why this method exhibits such strong performance
in practical settings while imposing an implausible assumption; namely, that
class conditional distributions of pre-trained features have tied covariance.
Although the Mahalanobis distance-based method is claimed to be motivated by
classification prediction confidence, we find that its superior performance
stems from information not useful for classification. This suggests that the
reason the Mahalanobis confidence score works so well is mistaken, and makes
use of different information from ODIN, another popular OoD detection method
based on prediction confidence. This perspective motivates us to combine these
two methods, and the combined detector exhibits improved performance and
robustness. These findings provide insight into the behavior of neural
classifiers in response to anomalous inputs.

分析了基于马氏距离的置信分数方法的性能表现，发现其表现卓越的原因并非在于分类预测置信度，而在于其利用了不同于 ODIN 方法的有用信息。结合这两种方法可提高检测器的性能和鲁棒性。