动态调整阈值，让模型更准识别异常数据。
AdaSCALE: Adaptive Scaling for OOD Detection
- 根据样本疑似异常程度自适应调节激活缩放阈值。
- 在ImageNet-1k上近域和远域数据分别提升14.94%和21.67%。
- 适合需要高可靠性的实际部署场景使用。
深度学习模型识别输入是否超出训练分布的能力对安全可靠部署至关重要。当前最先进的分布外（OOD）检测方法利用激活形状增强分布内（ID）与分布外输入的分离度，但这些方法虽采用样本特异性缩放，却对所有样本使用固定的百分位阈值，导致分离效果不理想。本文提出AdaSCALE，一种基于样本估计的分布外可能性动态调整百分位阈值的自适应缩放方法。关键观察是：在微小扰动下，异常样本在高激活值处的激活偏移比正常样本更显著。该方法对可能为正常样本的输入施加强缩放，对可能为异常样本的输入施加弱缩放，从而生成高度可分的能量分数。在ImageNet-1k基准测试中，跨八种不同架构，平均FPR@95指标上，本方法相比最新对比方法OptFS，在近域数据集上提升14.94%，远域数据集上提升21.67%。代码已公开于https://github.com/sudarshanregmi/AdaSCALE/
原文摘要 · Abstract (English)
The ability of the deep learning model to recognize when a sample falls outside its learned distribution is critical for safe and reliable deployment. Recent state-of-the-art out-of-distribution (OOD) detection methods leverage activation shaping to improve the separation between in-distribution (ID) and OOD inputs. These approaches resort to sample-specific scaling but apply a static percentile threshold across all samples regardless of their nature, resulting in suboptimal ID-OOD separability. In this work, we propose \textbf{AdaSCALE}, an adaptive scaling procedure that dynamically adjusts the percentile threshold based on a sample's estimated OOD likelihood. This estimation leverages our key observation: OOD samples exhibit significantly more pronounced activation shifts at high-magnitude activations under minor perturbation compared to ID samples. AdaSCALE enables stronger scaling for likely ID samples and weaker scaling for likely OOD samples, yielding highly separable energy scores. Our approach achieves state-of-the-art OOD detection performance, outperforming the latest rival OptFS by 14.94% in near-OOD and 21.67% in far-OOD datasets in average FPR@95 metric on the ImageNet-1k benchmark across eight diverse architectures. The code is available at: https://github.com/sudarshanregmi/AdaSCALE/
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