arXiv cs.AI· Muhammad Aqeel, Maham Nazir, Marco Cristani, Francesco Setti·· 11 小时前AI 评分27
DART：面向通用异常检测的领域无关残差迁移框架
DART: Domain-Agnostic Residual Transfer for Generalist Anomaly Detection
AI 导读
DART 是一个即插即用的通用异常检测框架，可将残差分离为异常相关与领域特定两部分，缓解纹理、几何、采集条件等外观因素造成的跨域纠缠。它结合残差解耦模块与跨域元训练，可插入现有 GAD 流程的残差计算与检测头之间。在 MVTec AD、VisA 和 BraTS 上，DART 相对多个基线方法取得一致提升，域偏移越强增益越大。
正文 · 原文
Abstract:Generalist Anomaly Detection (GAD) aims to detect anomalies in unseen domains using only a few reference samples. Recent normal-abnormal guided methods improve this setting by exploiting abnormal references, but their residuals often mix transferable anomaly cues with domain-specific appearance factors such as texture, geometry, and acquisition conditions. This residual domain entanglement limits cross-domain transfer and may produce spurious activations on novel domains. We propose DART, a plug-in framework for normal-abnormal guided GAD that learns to separate residuals into anomaly-relevant and domain-specific components. DART combines a Residual Disentanglement Module, which preserves anomaly-discriminative information while suppressing domain information, with Cross-Domain Meta-Training, which forms episodes across source domains to make the separation effective. The method is architecture-agnostic and can be inserted between residual computation and the detection head of existing GAD pipelines. Experiments on MVTec AD, VisA, and BraTS show consistent improvements over multiple base methods, with the largest gains under stronger domain shifts. this https URL
| Comments: | Accepted to NeurIPS 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.08271 [cs.CV] |
| (or arXiv:2611.08271v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08271 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muhammad Aqeel [view email]
[v1]
Thu, 5 Nov 2026 16:43:20 UTC (1,796 KB)
来源：arXiv cs.AI · arxiv.org