Detecting out-of-distribution (OOD) inputs is critical for safely deploying
deep learning models in real-world scenarios. In recent years, many OOD
detectors have been developed, and even the benchmarking has been standardized,
i.e. OpenOOD. The number of post-hoc detectors is growing fast and showing an
option to protect a pre-trained classifier against natural distribution shifts,
claiming to be ready for real-world scenarios. However, its efficacy in
handling adversarial examples has been neglected in the majority of studies.
This paper investigates the adversarial robustness of the 16 post-hoc detectors
on several evasion attacks and discuss a roadmap towards adversarial defense in
OOD detectors.

研究检测和防御方法，以保护深度学习模型免受不符合预期数据、对抗性示例和逃避攻击的影响。

解读后续离群检测器对于对抗鲁棒性的定义

Deciphering the Definition of Adversarial Robustness for post-hoc OOD  Detectors

Out-of-distribution (OOD) detection is essential for the safe deployment of
AI. Particularly, OOD detectors should generalize effectively across diverse
scenarios. To improve upon the generalizability of existing OOD detectors, we
introduce a highly versatile OOD detector, called Neural Collapse inspired OOD
detector (NC-OOD). We extend the prevalent observation that in-distribution
(ID) features tend to form clusters, whereas OOD features are far away.
Particularly, based on the recent observation, Neural Collapse, we further
demonstrate that ID features tend to cluster in proximity to weight vectors.
From our extended observation, we propose to detect OOD based on feature
proximity to weight vectors. To further rule out OOD samples, we leverage the
observation that OOD features tend to reside closer to the origin than ID
features. Extensive experiments show that our approach enhances the
generalizability of existing work and can consistently achieve state-of-the-art
OOD detection performance across a wide range of OOD Benchmarks over different
classification tasks, training losses, and model architectures.

基于 Neural Collapse 观察的特征向量相似度，我们提出了一种高度灵活的 Neural Collapse inspired OOD 检测器（NC-OOD），通过利用 ID 特征聚类性和 OOD 特征与原点的距离性，显著提高了现有工作的泛化能力，同时在不同分类任务、训练损失和模型架构的广泛实验中，始终实现了最新的 OOD 检测性能。

通过神经崩溃的视角检测超出分布范围

Detecting Out-of-Distribution Through the Lens of Neural Collapse

Existing Out-of-Distribution (OoD) detection methods address to detect OoD
samples from In-Distribution data (InD) mainly by exploring differences in
features, logits and gradients in Deep Neural Networks (DNNs). We in this work
propose a new perspective upon loss landscape and mode ensemble to investigate
OoD detection. In the optimization of DNNs, there exist many local optima in
the parameter space, or namely modes. Interestingly, we observe that these
independent modes, which all reach low-loss regions with InD data (training and
test data), yet yield significantly different loss landscapes with OoD data.
Such an observation provides a novel view to investigate the OoD detection from
the loss landscape and further suggests significantly fluctuating OoD detection
performance across these modes. For instance, FPR values of the RankFeat method
can range from 46.58% to 84.70% among 5 modes, showing uncertain detection
performance evaluations across independent modes. Motivated by such diversities
on OoD loss landscape across modes, we revisit the deep ensemble method for OoD
detection through mode ensemble, leading to improved performance and benefiting
the OoD detector with reduced variances. Extensive experiments covering varied
OoD detectors and network structures illustrate high variances across modes and
also validate the superiority of mode ensemble in boosting OoD detection. We
hope this work could attract attention in the view of independent modes in the
OoD loss landscape and more reliable evaluations on OoD detectors.

通过对损失景观和模态集进行研究，提出了一种新的视角来调查面向损失景观的异常样本检测，重访了深度集成方法以提高异常检测性能和减小变异，并通过广泛的实验证明了模态的高度变化和模态集成在提升异常检测中的优越性。

基于损失景观角度重新审视深度集成用于外部分布检测

Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss  Landscape Perspective

The task of out-of-distribution (OOD) detection is notoriously ill-defined.
Earlier works focused on new-class detection, aiming to identify label-altering
data distribution shifts, also known as "semantic shift." However, recent works
argue for a focus on failure detection, expanding the OOD evaluation framework
to account for label-preserving data distribution shifts, also known as
"covariate shift." Intriguingly, under this new framework, complex OOD
detectors that were previously considered state-of-the-art now perform
similarly to, or even worse than the simple maximum softmax probability
baseline. This raises the question: what are the latest OOD detectors actually
detecting? Deciphering the behavior of OOD detection algorithms requires
evaluation datasets that decouples semantic shift and covariate shift. To aid
our investigations, we present ImageNet-OOD, a clean semantic shift dataset
that minimizes the interference of covariate shift. Through comprehensive
experiments, we show that OOD detectors are more sensitive to covariate shift
than to semantic shift, and the benefits of recent OOD detection algorithms on
semantic shift detection is minimal. Our dataset and analyses provide important
insights for guiding the design of future OOD detectors.

通过广泛的实验，我们展示了当前的 ODD 检测器对于协变量转移比语义转移更为敏感，并且最新的 ODD 检测算法对于语义转移的检测效果微乎其微。我们的数据集和分析为指导未来的 ODD 检测器设计提供了重要见解。