通过提取激活图的均值和方差，提升模型对异常输入的检测能力。
DAVIS: OOD Detection via Dominant Activations and Variance for Increased Separation
- 利用通道级方差与最大激活值增强特征表示
- 在CIFAR-10/100/ImageNet上显著降低误报率
- 适用于ResNet、DenseNet等主流架构，无需训练
检测分布外（OOD）输入是机器学习模型在真实场景部署中的关键保障。然而，多数后处理检测方法依赖全局平均池化（GAP）得到的特征表示——这一过程会丢失激活图中的重要分布统计信息。本文认为，被忽略的通道级方差和主导（最大）激活值对OOD检测具有高度判别性。我们提出DAVIS，一种简单且通用的后处理技术，通过引入这些关键统计量丰富特征向量，直接缓解GAP带来的信息损失。大量实验表明，DAVIS在多种架构（包括ResNet、DenseNet、EfficientNet）上均达到新基准。在CIFAR-10上使用ResNet-18时FPR95降低48.26%，CIFAR-100上使用ResNet-34降低38.13%，ImageNet-1k上使用MobileNet-v2降低26.83%。分析揭示了性能提升的内在机制，为超越均值的OOD检测提供了理论基础。
原文摘要 · Abstract (English)
Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc detection methods operate on penultimate feature representations derived from global average pooling (GAP) -- a lossy operation that discards valuable distributional statistics from activation maps prior to global average pooling. We contend that these overlooked statistics, particularly channel-wise variance and dominant (maximum) activations, are highly discriminative for OOD detection. We introduce DAVIS, a simple and broadly applicable post-hoc technique that enriches feature vectors by incorporating these crucial statistics, directly addressing the information loss from GAP. Extensive evaluations show DAVIS sets a new benchmark across diverse architectures, including ResNet, DenseNet, and EfficientNet. It achieves significant reductions in the false positive rate (FPR95), with improvements of 48.26\% on CIFAR-10 using ResNet-18, 38.13\% on CIFAR-100 using ResNet-34, and 26.83\% on ImageNet-1k benchmarks using MobileNet-v2. Our analysis reveals the underlying mechanism for this improvement, providing a principled basis for moving beyond the mean in OOD detection.
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