什么发生了变化？基于真实、虚拟与不可比诊断的漂移检测
What Changed? Drift Detection with Real, Virtual, and Incomparable Diagnosis
- Kagoshima University（鹿儿岛大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究提出双轴漂移检测门，结合功能轴与新颖性轴，解决深度交换分数混淆问题，在广义类别发现中实现高AUROC，并验证于多种骨干网络。
中文摘要 AI 辅助
共享深度编码器本身并不能解决任务比较分数的核心混淆问题。我们证明，在冻结的共享表示上交叉评估的头部继承了浅层交换分数的外推混淆：固定标签的纯输入旋转将深度交换分数从约0膨胀到0.80，而表示新颖性分数在互补方向上失明（在完全改变任务的标签排列下保持平坦）。将条件双判别器差异移植到嵌入空间解决了两个盲点：功能轴在旋转下保持在±0.001以内，并单调跟踪标签排列漂移质量。构建到混合头部生命周期中，双轴门在匹配的训练预算下，以更少的头部实现了比交换或新颖性触发器更好的决策质量。在广义类别发现中，相同的块级功能轴以AUROC 0.98-0.99将语义新颖性与光度偏移区分开来，而每个输入的OOD分数（MSP、Energy、Mahalanobis、KNN）在该区分上接近随机。所有发现均在冻结的ImageNet-21k ViT-B/16和自监督DINOv2骨干上在CIFAR-100上重复，并扩展到具有循环的残差适配器池，其中零校准的新颖性触发器在机制变化时从不触发，而双轴门以完全循环重用处理它们。我们明确陈述了嵌入空间结论转移到原始机制的公共因子分解条件。
英文摘要
Sharing a deep encoder does not, by itself, fix the central confound of task-comparison scores. We show that cross-evaluated heads on a frozen shared representation inherit the extrapolation confound of shallow exchange scores: pure input rotations with fixed labels inflate a deep exchange score from about 0 to 0.80, while representation-novelty scores are blind in the complementary direction (flat under label permutations that change the task completely). Transplanting a conditional two-discriminator discrepancy into the embedding space resolves both blind spots: the functional axis stays within +-0.001 under rotations and tracks label-permutation drift mass monotonically. Built into a mixture-of-heads lifecycle, the two-axis gate attains better decision quality with fewer heads than exchange or novelty triggers at a matched training budget. On generalized category discovery, the same chunk-level functional axis separates semantic novelty from photometric shift with AUROC 0.98-0.99 where per-input OOD scores (MSP, Energy, Mahalanobis, KNN) sit near chance for that distinction. All findings replicate across frozen ImageNet-21k ViT-B/16 and self-supervised DINOv2 backbones on CIFAR-100, and extend to residual adapter pools with recurrence, where a null-calibrated novelty trigger never fires on mechanism changes while the two-axis gate handles them with full recurrence reuse. We state explicitly the common-factoring condition under which embedding-space conclusions transfer to the original mechanism.
