| US | Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings <br> | CVPR | 2020 | Code | Teacher-student architecture |
| MKD | Multiresolution knowledge distillation for anomaly detection <br> | CVPR | 2021 | Code | Teacher-student architecture |
| GP | Glancing at the patch: Anomaly localization with global and local feature comparison <br> | CVPR | 2021 | - | Teacher-student architecture |
| RD4AD | Anomaly Detection via Reverse Distillation From One-Class Embedding <br> | CVPR | 2022 | Code | Teacher-student architecture |
| PFM | Unsupervised Image Anomaly Detection and Segmentation Based on Pretrained Feature Mapping <br> | TII | 2023 | Code | Teacher-student architecture |
| MemKD | Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection <br> | ICCV | 2023 | Code | Teacher-student architecture |
| DeSTSeg | DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection <br> | CVPR | 2023 | Code | Teacher-student architecture |
| EfficientAD | EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies <br> | WACV | 2024 | Unofficial Code | Teacher-student architecture |
| EMMFRKD | Enhanced multi-scale features mutual mapping fusion based on reverse knowledge distillation for industrial anomaly detection and localization <br> | TBD | 2024 | - | Teacher-student architecture |
| AEKD | AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection <br> | JMS | 2024 | - | Teacher-student architecture |
| FCACDL | Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection <br> | ICASSP | 2024 | - | Teacher-student architecture |
| DMDD | Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection <br> | ACM MM | 2024 | - | Teacher-student architecture |
| CutPaste | CutPaste: Self-Supervised Learning for Anomaly Detection and Localization <br> | CVPR | 2021 | Unofficial Code | One-class classification |
| SimpleNet | SimpleNet: A Simple Network for Image Anomaly Detection and Localization <br> | CVPR | 2023 | Code | One-class classification |
| ADShift | Anomaly Detection Under Distribution Shift <br> | ICCV | 2023 | Code | One-class classification |
| DS2 | Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining <br> | WACV | 2024 | - | One-class classification |
| GeneralAD | GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features <br> | ECCV | 2024 | Code | One-class classification |
| GLASS | A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization <br> | ECCV | 2024 | Code | One-class classification |
| FastFlow | FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows <br> | - | 2021 | Unofficial Code | Distribution map |
| DifferNet | Same Same but DifferNet: Semi-Supervised Defect Detection With Normalizing Flows <br> | WACV | 2021 | Code | Distribution map |
| CFLOW-AD | CFLOW-AD: Real-Time Unsupervised Anomaly Detection With Localization via Conditional Normalizing Flows <br> | WACV | 2022 | Code | Distribution map |
| CS-Flow | Fully Convolutional Cross-Scale-Flows for Image-Based Defect Detection <br> | WACV | 2022 | Code | Distribution map |
| CDO | Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization <br> | TII | 2023 | Code | Distribution map |
| PyramidFlow | PyramidFlow: High-Resolution Defect Contrastive Localization Using Pyramid Normalizing Flow <br> | CVPR | 2023 | Code | Distribution map |
| SLAD | Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning <br> | ICML | 2023 | Code | Distribution map |
| MSFlow | MSFlow: Multiscale Flow-Based Framework for Unsupervised Anomaly Detection <br> | TNNLS | 2024 | Code | Distribution map |
| AttentDifferNet | Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study <br> | WACV | 2024 | Code | Distribution map |
| PaDiM | PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization <br> | ICPR | 2021 | Unofficial Code | Memory bank |
| PatchCore | Towards Total Recall in Industrial Anomaly Detection <br> | CVPR | 2022 | Code | Memory bank |
| CFA | CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization <br> | IEEE Access | 2022 | Code | Memory bank |
| DMAD | Diversity-Measurable Anomaly Detection <br> | CVPR | 2023 | Code | Memory bank |
| PNI | PNI : Industrial Anomaly Detection using Position and Neighborhood Information <br> | ICCV | 2023 | Code | Memory bank |
| GraphCore | Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore <br> | ICLR | 2023 | - | Memory bank |
| InReaCh | Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification <br> | ICCV | 2023 | Code | Memory bank |
| ReconFA | A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation <br> | ICASSP | 2024 | - | Memory bank |
| ReConPatch | ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection <br> | WACV | 2024 | Unofficial Code | Memory bank |
| AE-SSIM | Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders <br> | - | 2018 | Unofficial Code | Autoencoder-based Reconstruction |
| DFR | Unsupervised anomaly segmentation via deep feature reconstruction <br> | Neurocomputing | 2020 | Code | Autoencoder-based Reconstruction |
| DAAD | Divide-and-Assemble: Learning Block-Wise Memory for Unsupervised Anomaly Detection <br> | ICCV | 2021 | - | Autoencoder-based Reconstruction |
| RIAD | Reconstruction by inpainting for visual anomaly detection <br> | PR | 2021 | Unofficial Code | Autoencoder-based Reconstruction |
| DRÆM | DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection <br> | ICCV | 2021 | Code | Autoencoder-based Reconstruction |
| DSR | DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection <br> | ECCV | 2022 | Code | Autoencoder-based Reconstruction |
| NSA | Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization <br> | ECCV | 2022 | Code | Autoencoder-based Reconstruction |
| SSPCAB | Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection <br> | CVPR | 2022 | Code | Autoencoder-based Reconstruction |
| SSMCTB | Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection <br> | TPAMI | 2024 | Code | Autoencoder-based Reconstruction |
| THFR | Template-guided Hierarchical Feature Restoration for Anomaly Detection <br> | ICCV | 2023 | - | Autoencoder-based Reconstruction |
| FastRecon | FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction <br> | ICCV | 2023 | Code | Autoencoder-based Reconstruction |
| RealNet | RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection <br> | CVPR | 2024 | Code | Autoencoder-based Reconstruction |
| IFgNet | Implicit Foreground-Guided Network for Anomaly Detection and Localization <br> | ICASSP | 2024 | Code | Autoencoder-based Reconstruction |
| LAMP | Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification <br> | ICASSP | 2024 | - | Autoencoder-based Reconstruction |
| PatchAnomaly | Patch-Wise Augmentation for Anomaly Detection and Localization <br> | ICASSP | 2024 | - | Autoencoder-based Reconstruction |
| MAAE | Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection <br> | ICASSP | 2024 | - | Autoencoder-based Reconstruction |
| DC-AE | Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection <br> | TII | 2024 | - | Autoencoder-based Reconstruction |
| SCADN | Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection <br> | AAAI | 2021 | Code | GAN-based Reconstruction |
| OCR-GAN | Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection <br> | TIP | 2023 | Code | GAN-based Reconstruction |
| MeTAL | Masked Transformer for Image Anomaly Localization <br> | IJNS | 2022 | - | Transformer-based Reconstruction |
| FOD | Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection <br> | ICCV | 2023 | Code | Transformer-based Reconstruction |
| AMI-Net | AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization <br> | TASE | 2024 | Code | Transformer-based Reconstruction |
| PNPT | Prior Normality Prompt Transformer for Multiclass Industrial Image Anomaly Detection <br> | TII | 2024 | - | Transformer-based Reconstruction |
| DDAD | Anomaly Detection with Conditioned Denoising Diffusion Models <br> | - | 2023 | Code | Diffusion-based Reconstruction |
| DiffAD | Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model <br> | ICCV | 2023 | - | Diffusion-based Reconstruction |
| RAN | Removing Anomalies as Noises for Industrial Defect Localization <br> | ICCV | 2023 | - | Diffusion-based Reconstruction |
| TransFusion | TransFusion – A Transparency-Based Diffusion Model for Anomaly Detection <br> | ECCV | 2024 | Code | Diffusion-based Reconstruction |
| DiAD | A Diffusion-Based Framework for Multi-Class Anomaly Detection <br> | AAAI | 2024 | Code | Diffusion-based Reconstruction |
| GLAD | GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection <br> | ECCV | 2024 | Code | Diffusion-based Reconstruction |
| AnomalySD | AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model <br> | - | 2024 | - | Diffusion-based Reconstruction |
