# Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization

arXiv: https://arxiv.org/abs/2402.17091 · HTML: https://mlsanity.com/paper/2402.17091/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation.
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Placed in

- Knowledge distillation / [Detection and dense prediction distillation](https://mlsanity.com/distill/detection-and-dense-prediction-distillation/)

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Structured Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/1903.04197/) (2019)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [A Unified Model for Multi-class Anomaly Detection](https://mlsanity.com/paper/2206.03687/) (2022)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization](https://mlsanity.com/paper/2206.04325/) (2022)
- [Learning Normal Dynamics in Videos with Meta Prototype Network](https://mlsanity.com/paper/2104.06689/) (2021)
- [DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2012.07122/) (2020)
- [Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection](https://mlsanity.com/paper/2207.10948/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Survey on Foundation-Model-Based Industrial Defect Detection](https://mlsanity.com/paper/2502.19106/) (2025)
- [Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2405.04782/) (2024)
- [Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection](https://mlsanity.com/paper/2405.06467/) (2024)
- [Test-Time Adaptation for Anomaly Segmentation via Topology-Aware Optimal Transport Chaining](https://mlsanity.com/paper/2601.20333/) (2026)
- [Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation](https://mlsanity.com/paper/2606.28268/) (2026)

## Abstract (arXiv)

Visual anomaly detection is a challenging open-set task aimed at identifying unknown anomalous patterns while modeling normal data. The knowledge distillation paradigm has shown remarkable performance in one-class anomaly detection by leveraging teacher-student network feature comparisons. However, extending this paradigm to multi-class anomaly detection introduces novel scalability challenges. In this study, we address the significant performance degradation observed in previous teacher-student models when applied to multi-class anomaly detection, which we identify as resulting from cross-class interference. To tackle this issue, we introduce a novel approach known as Structural Teacher-Student Normality Learning (SNL): (1) We propose spatial-channel distillation and intra-&inter-affinity distillation techniques to measure structural distance between the teacher and student networks. (2) We introduce a central residual aggregation module (CRAM) to encapsulate the normal representation space of the student network. We evaluate our proposed approach on two anomaly detection datasets, MVTecAD and VisA. Our method surpasses the state-of-the-art distillation-based algorithms by a significant margin of 3.9% and 1.5% on MVTecAD and 1.2% and 2.5% on VisA in the multi-class anomaly detection and localization tasks, respectively. Furthermore, our algorithm outperforms the current state-of-the-art unified models on both MVTecAD and VisA.