用双模型蒸馏实现跨领域异常检测，兼顾工业与语义异常识别。
Generalist Multi-Class Anomaly Detection via Distillation to Two Heterogeneous Student Networks
- 通过两个异构学生网络分别处理局部缺陷和语义异常，共享预训练编码器提取特征。
- 在8个数据集上均达领先性能，图像级AUROC最高达99.7%（MVTec-AD）。
- 适合需要通用异常检测能力的研究与工业应用，尤其多类别场景。
异常检测在众多实际应用中至关重要。然而，现有方法往往偏向工业检测，难以泛化到语义异常检测等更广泛任务。尽管已有研究尝试解决通用异常检测问题，其性能仍对数据集特定设置敏感且受限于单类别任务。本文提出一种基于知识蒸馏的双模型集成新方法，框架包含一个教师模型和两个学生模型：一个用于工业异常检测的编码器-解码器模型，擅长发现像素级微小缺陷；另一个为优化语义异常检测的编码器-编码器模型。两者共享预训练编码器（DINOv2）以提取高质量特征表示。通过噪声或（Noisy-OR）目标联合训练，最终异常分数由各自模型生成的局部与语义异常得分的联合概率确定。我们在八个公开基准上评估了该方法，涵盖单类与多类设置：工业检测任务使用MVTec-AD、MVTec-LOCO、VisA和Real-IAD；语义异常检测任务使用CIFAR-10/100、FMNIST和View。所提方法在两类任务中均达到最优性能，尤其在多类设置下显著优于现有通用模型，甚至超越个别专用模型。在MVTec-AD上图像级AUROC达99.7%，在CIFAR-10上达97.8%，验证了其跨域泛化能力。
原文摘要 · Abstract (English)
Anomaly detection (AD) plays an important role in various real-world applications. Recent advancements in AD, however, are often biased towards industrial inspection, struggle to generalize to broader tasks like semantic anomaly detection and vice versa. Although recent methods have attempted to address general anomaly detection, their performance remains sensitive to dataset-specific settings and single-class tasks. In this paper, we propose a novel dual-model ensemble approach based on knowledge distillation (KD) to bridge this gap. Our framework consists of a teacher and two student models: an Encoder-Decoder model, specialized in detecting patch-level minor defects for industrial AD and an Encoder-Encoder model, optimized for semantic AD. Both models leverage a shared pre-trained encoder (DINOv2) to extract high-quality feature representations. The dual models are jointly learned using the Noisy-OR objective, and the final anomaly score is obtained using the joint probability via local and semantic anomaly scores derived from the respective models. We evaluate our method on eight public benchmarks under both single-class and multi-class settings: MVTec-AD, MVTec-LOCO, VisA and Real-IAD for industrial inspection and CIFAR-10/100, FMNIST and View for semantic anomaly detection. The proposed method achieved state-of-the-art accuracies in both domains, in multi-class as well as single-class settings, demonstrating generalization across multiple domains of anomaly detection. Our model achieved an image-level AUROC of 99.7% on MVTec-AD and 97.8% on CIFAR-10, which is significantly better than the prior general AD models in multi-class settings and even higher than the best specialist models on individual benchmarks.
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