{"task":"Anomaly Detection","dataset":"MPDD","metric_names":["Detection AUROC","Segmentation AUROC","Segmentation AUPRO"],"rows":[{"id":19736,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"GLASS","metrics":{"Detection AUROC":"99.6","Segmentation AUPRO":"98.2","Segmentation AUROC":"99.4"},"paper_url":"https://arxiv.org/abs/2407.09359v1","paper_title":"A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization","paper_date":"2024-07-12","code_links":[{"title":"cqylunlun/glass","url":"https://github.com/cqylunlun/glass"},{"title":"septmars/DL","url":"https://github.com/septmars/DL"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19737,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"CRAS","metrics":{"Detection AUROC":"98.8","Segmentation AUROC":"98.7"},"paper_url":"https://arxiv.org/abs/2505.17551v1","paper_title":"Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection","paper_date":"2025-05-23","code_links":[{"title":"cqylunlun/CRAS","url":"https://github.com/cqylunlun/CRAS"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19738,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"DMDD","metrics":{"Detection AUROC":"98.10","Segmentation AUPRO":"97.66","Segmentation AUROC":"98.96"},"paper_url":"https://arxiv.org/abs/2408.03888v2","paper_title":"Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection","paper_date":"2024-08-07","code_links":[],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19739,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"PBAS","metrics":{"Detection AUROC":"97.7","Segmentation AUPRO":"97.1","Segmentation AUROC":"98.8"},"paper_url":"https://arxiv.org/abs/2412.17458v1","paper_title":"Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection","paper_date":"2024-12-23","code_links":[{"title":"cqylunlun/pbas","url":"https://github.com/cqylunlun/pbas"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19740,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"GLAD","metrics":{"Detection AUROC":"97.5","Segmentation AUROC":"98.7"},"paper_url":"https://arxiv.org/abs/2406.07487v3","paper_title":"GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection","paper_date":"2024-06-11","code_links":[{"title":"hyao1/glad","url":"https://github.com/hyao1/glad"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19741,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"POUTA","metrics":{"Detection AUROC":"97.5"},"paper_url":"https://arxiv.org/abs/2312.12913v1","paper_title":"Produce Once, Utilize Twice for Anomaly Detection","paper_date":"2023-12-20","code_links":[],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19742,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"Dinomaly","metrics":{"Detection AUROC":"97.2","Segmentation AUROC":"99.1"},"paper_url":"https://arxiv.org/abs/2405.14325v4","paper_title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","paper_date":"2024-05-23","code_links":[{"title":"guojiajeremy/dinomaly","url":"https://github.com/guojiajeremy/dinomaly"},{"title":"septmars/DL","url":"https://github.com/septmars/DL"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19743,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"RealNet","metrics":{"Detection AUROC":"96.3","Segmentation AUROC":"98.2"},"paper_url":"https://arxiv.org/abs/2403.05897v1","paper_title":"RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection","paper_date":"2024-03-09","code_links":[{"title":"cnulab/realnet","url":"https://github.com/cnulab/realnet"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":1,"source":"archive","tags":[]},{"id":19744,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"DiffusionAD","metrics":{"Detection AUROC":"96.2","Segmentation AUPRO":"95.3","Segmentation AUROC":"98.5"},"paper_url":"https://arxiv.org/abs/2303.08730v4","paper_title":"DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection","paper_date":"2023-03-15","code_links":[{"title":"huizhang0812/diffusionad","url":"https://github.com/huizhang0812/diffusionad"},{"title":"HuiZhang0812/DiffusionAD-Denoising-Diffusion-for-Anomaly-Detection","url":"https://github.com/HuiZhang0812/DiffusionAD-Denoising-Diffusion-for-Anomaly-Detection"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19745,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"ULSAD","metrics":{"Detection AUROC":"95.73","Segmentation AUPRO":"92.02","Segmentation AUROC":"97.45"},"paper_url":"https://arxiv.org/abs/2410.16255v1","paper_title":"Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection","paper_date":"2024-10-21","code_links":[{"title":"sukanyapatra1997/ulsad-2024","url":"https://github.com/sukanyapatra1997/ulsad-2024"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19746,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"LeMO","metrics":{"Detection AUROC":"87.4","Segmentation AUPRO":"91.9","Segmentation AUROC":"97.8"},"paper_url":"https://arxiv.org/abs/2305.15652v1","paper_title":"Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision","paper_date":"2023-05-25","code_links":[],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19747,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"FastRecon","metrics":{"Detection AUROC":"82.5","Segmentation AUROC":"97.9"},"paper_url":"http://openaccess.thecvf.com//content/ICCV2023/html/Fang_FastRecon_Few-shot_Industrial_Anomaly_Detection_via_Fast_Feature_Reconstruction_ICCV_2023_paper.html","paper_title":"FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction","paper_date":"2023-01-01","code_links":[],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19748,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"AdaCLIP","metrics":{"Detection AUROC":"82.5","Segmentation AUROC":"96.1"},"paper_url":"https://arxiv.org/abs/2407.15795v1","paper_title":"AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection","paper_date":"2024-07-22","code_links":[{"title":"caoyunkang/adaclip","url":"https://github.com/caoyunkang/adaclip"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19749,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"PatchCore","metrics":{"Detection AUROC":"82.12","Segmentation AUROC":"95.66"},"paper_url":"https://arxiv.org/abs/2106.08265v2","paper_title":"Towards Total Recall in Industrial Anomaly Detection","paper_date":"2021-06-15","code_links":[{"title":"openvinotoolkit/anomalib","url":"https://github.com/openvinotoolkit/anomalib"},{"title":"amazon-science/patchcore-inspection","url":"https://github.com/amazon-science/patchcore-inspection"},{"title":"amazon-research/patchcore-inspection","url":"https://github.com/amazon-research/patchcore-inspection"},{"title":"hcw-00/PatchCore_anomaly_detection","url":"https://github.com/hcw-00/PatchCore_anomaly_detection"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/patchcore"},{"title":"rvorias/ind_knn_ad","url":"https://github.com/rvorias/ind_knn_ad"},{"title":"OpenAOI/anodet","url":"https://github.com/OpenAOI/anodet"},{"title":"Burf/tfdetection","url":"https://github.com/Burf/tfdetection"},{"title":"tbcvContributor/DeepHawkeye","url":"https://github.com/tbcvContributor/DeepHawkeye"},{"title":"Ultranity/Anomaly.Paddle","url":"https://github.com/Ultranity/Anomaly.Paddle"},{"title":"tiskw/patchcore-ad","url":"https://github.com/tiskw/patchcore-ad"},{"title":"any-tech/PatchCore-ex","url":"https://github.com/any-tech/PatchCore-ex"},{"title":"taikiinoue45/PatchCore","url":"https://github.com/taikiinoue45/PatchCore"},{"title":"JoegameZhou/PatchCore","url":"https://github.com/JoegameZhou/PatchCore"},{"title":"captainfffsama/pathcore","url":"https://github.com/captainfffsama/pathcore"},{"title":"yangyucheng000/patchcore","url":"https://github.com/yangyucheng000/patchcore"},{"title":"totoroKalic/patchcore-mindspore","url":"https://github.com/totoroKalic/patchcore-mindspore"},{"title":"2023-MindSpore-1/ms-code-4","url":"https://github.com/2023-MindSpore-1/ms-code-4/tree/main/PatchCore"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19750,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"KAnoCLIP","metrics":{"Detection AUROC":"77.8","Segmentation AUROC":"98.3"},"paper_url":"https://arxiv.org/abs/2501.03786v1","paper_title":"KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration","paper_date":"2025-01-07","code_links":[],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]},{"id":19751,"task":"Anomaly Detection","parent_task":null,"dataset":"MPDD","model_name":"ADSPR","metrics":{"Segmentation AUROC":"96.8"},"paper_url":"http://openaccess.thecvf.com//content/ICCV2023/html/Shin_Anomaly_Detection_using_Score-based_Perturbation_Resilience_ICCV_2023_paper.html","paper_title":"Anomaly Detection using Score-based Perturbation Resilience","paper_date":"2023-01-01","code_links":[{"title":"Lee-JongHyeon/Anomaly-Detection-using-Score-based-Perturbation-Resilience","url":"https://github.com/Lee-JongHyeon/Anomaly-Detection-using-Score-based-Perturbation-Resilience"}],"metrics_order":"[\"Detection AUROC\", \"Segmentation AUROC\", \"Segmentation AUPRO\"]","area":"Methodology","uses_additional_data":0,"source":"archive","tags":[]}]}