---
id: 20260524-T0-08
title: "CenterLoss损害OOD检测，多尺度马氏距离方法效果更优"
title_en: "CenterLoss Hurts OOD Detection, Multi-Scale Mahalanobis Wins"
url: https://ai.daily.yangsir.net/daily/20260524-T0-08
issue_date: 2026-05-24
publish_date: 2026-05-23T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.21493
---

# CenterLoss损害OOD检测，多尺度马氏距离方法效果更优

最新研究指出，CenterLoss方法会损害机器学习系统的OOD检测能力。传统方法专注于分类准确率优化特征表示，而arXiv论文提出多尺度马氏距离方法能更好识别分布外数据。研究证明，分类准确率与OOD检测能力存在权衡关系，新方法在保持分类性能的同时显著提升了异常检测效果。

## English Version

**CenterLoss Hurts OOD Detection, Multi-Scale Mahalanobis Wins**

New research reveals CenterLoss harms OOD detection in ML systems. Current methods optimize features solely for classification accuracy, neglecting OOD detection capability. The arXiv paper proposes multi-scale Mahalanobis distance as a superior alternative, demonstrating better performance in identifying out-of-distribution data while maintaining classification accuracy.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.21493)

**详情页**：https://ai.daily.yangsir.net/daily/20260524-T0-08

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