{"url":"/task/out-of-distribution-detection","name":"Out-of-Distribution Detection","slug":"out-of-distribution-detection","description_markdown":"Detect out-of-distribution or anomalous examples.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":888,"papers_with_code":438,"benchmarks":53,"benchmark_tables_in_archive":53,"benchmark_tables_shown":53,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":24,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-10","slug":"out-of-distribution-detection-on-imagenet-1k-10","dataset":"ImageNet-1k vs Textures","dataset_url":"/dataset/imagenet-1k-vs-textures","rows_in_archive":34,"metrics":["AUROC","FPR95","Latency, ms"],"first_row_in_archive_order":{"model":"ViM (BiT)","paper_title":"ViM: Out-Of-Distribution with Virtual-logit Matching","paper_url":"/paper/vim-out-of-distribution-with-virtual-logit","paper_date":"2022-03-21","arxiv_id":"2203.10807","code_links":[{"title":"jingkang50/openood","url":"https://github.com/jingkang50/openood"},{"title":"haoqiwang/vim","url":"https://github.com/haoqiwang/vim"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-3","slug":"out-of-distribution-detection-on-imagenet-1k-3","dataset":"ImageNet-1k vs iNaturalist","dataset_url":"/dataset/imagenet-1k-vs-inaturalist-1","rows_in_archive":28,"metrics":["AUROC","FPR95","Latency, ms"],"first_row_in_archive_order":{"model":"Forte","paper_title":"Forte : Finding Outliers with Representation Typicality Estimation","paper_url":"/paper/forte-finding-outliers-with-representation","paper_date":"2024-10-02","arxiv_id":"2410.01322","code_links":[{"title":"DebarghaG/forte","url":"https://github.com/DebarghaG/forte"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-9","slug":"out-of-distribution-detection-on-imagenet-1k-9","dataset":"ImageNet-1k vs Places","dataset_url":"/dataset/imagenet-1k-vs-places","rows_in_archive":25,"metrics":["FPR95","AUROC"],"first_row_in_archive_order":{"model":"CMA(ViT-B/16, NegLabel)","paper_title":"Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations","paper_url":"/paper/enhanced-ood-detection-through-cross-modal","paper_date":"2025-03-24","arxiv_id":"2503.18817","code_links":[{"title":"ma-kjh/CMA-OoDD","url":"https://github.com/ma-kjh/CMA-OoDD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-8","slug":"out-of-distribution-detection-on-imagenet-1k-8","dataset":"ImageNet-1k vs SUN","dataset_url":"/dataset/imagenet-1k-vs-sun","rows_in_archive":22,"metrics":["FPR95","AUROC"],"first_row_in_archive_order":{"model":"CMA(ViT-B/16, NegLabel)","paper_title":"Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations","paper_url":"/paper/enhanced-ood-detection-through-cross-modal","paper_date":"2025-03-24","arxiv_id":"2503.18817","code_links":[{"title":"ma-kjh/CMA-OoDD","url":"https://github.com/ma-kjh/CMA-OoDD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-12","slug":"out-of-distribution-detection-on-imagenet-1k-12","dataset":"ImageNet-1k vs Curated OODs (avg.)","dataset_url":"/dataset/places365","rows_in_archive":16,"metrics":["FPR95","AUROC"],"first_row_in_archive_order":{"model":"NNGuide (RegNet)","paper_title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","paper_url":"/paper/nearest-neighbor-guidance-for-out-of-1","paper_date":"2023-09-26","arxiv_id":"2309.14888","code_links":[{"title":"jingkang50/openood","url":"https://github.com/jingkang50/openood"},{"title":"roomo7time/nnguide","url":"https://github.com/roomo7time/nnguide"}],"syntology":{"n":8,"n_ran":6,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs","slug":"out-of-distribution-detection-on-cifar-10-vs","dataset":"CIFAR-10 vs CIFAR-100","dataset_url":"/dataset/cifar-10","rows_in_archive":14,"metrics":["AUROC","AUPR","FPR95"],"first_row_in_archive_order":{"model":"DHM","paper_title":"Deep Hybrid Models for Out-of-Distribution Detection","paper_url":"/paper/deep-hybrid-models-for-out-of-distribution","paper_date":"2022-01-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs","slug":"out-of-distribution-detection-on-cifar-100-vs","dataset":"CIFAR-100 vs CIFAR-10","dataset_url":null,"rows_in_archive":14,"metrics":["AUROC","AUPR"],"first_row_in_archive_order":{"model":"DHM","paper_title":"Deep Hybrid Models for Out-of-Distribution Detection","paper_url":"/paper/deep-hybrid-models-for-out-of-distribution","paper_date":"2022-01-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10","slug":"out-of-distribution-detection-on-cifar-10","dataset":"CIFAR-10","dataset_url":"/dataset/cifar-10","rows_in_archive":10,"metrics":["AUROC","FPR95"],"first_row_in_archive_order":{"model":"DHM","paper_title":"Deep Hybrid Models for Out-of-Distribution Detection","paper_url":"/paper/deep-hybrid-models-for-out-of-distribution","paper_date":"2022-01-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-11","slug":"out-of-distribution-detection-on-imagenet-1k-11","dataset":"ImageNet-1k vs OpenImage-O","dataset_url":"/dataset/imagenet-k-vs-openimage-o","rows_in_archive":7,"metrics":["AUROC","FPR95","Latency, ms"],"first_row_in_archive_order":{"model":"NNGuide (RegNet)","paper_title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","paper_url":"/paper/nearest-neighbor-guidance-for-out-of-1","paper_date":"2023-09-26","arxiv_id":"2309.14888","code_links":[{"title":"jingkang50/openood","url":"https://github.com/jingkang50/openood"},{"title":"roomo7time/nnguide","url":"https://github.com/roomo7time/nnguide"}],"syntology":{"n":8,"n_ran":6,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-stl-10","slug":"out-of-distribution-detection-on-stl-10","dataset":"STL-10","dataset_url":"/dataset/stl-10","rows_in_archive":6,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"Mixup (Gaussian)","paper_title":"On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks","paper_url":"/paper/on-mixup-training-improved-calibration-and","paper_date":"2019-05-27","arxiv_id":"1905.11001","code_links":[{"title":"paganpasta/onmixup","url":"https://github.com/paganpasta/onmixup"},{"title":"MacroMayhem/OnMixup","url":"https://github.com/MacroMayhem/OnMixup"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-8","slug":"out-of-distribution-detection-on-cifar-100-vs-8","dataset":"CIFAR-100 vs SVHN","dataset_url":null,"rows_in_archive":5,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"OECC + MD","paper_title":"Outlier Exposure with Confidence Control for Out-of-Distribution Detection","paper_url":"/paper/simultaneous-classification-and-novelty","paper_date":"2019-06-08","arxiv_id":"1906.03509","code_links":[{"title":"nazim1021/OOD-detection-using-OECC","url":"https://github.com/nazim1021/OOD-detection-using-OECC"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-13","slug":"out-of-distribution-detection-on-imagenet-1k-13","dataset":"ImageNet-1k vs NINCO","dataset_url":"/dataset/imagenet-1k-vs-ninco","rows_in_archive":5,"metrics":["AUROC","FPR@95","Latency, ms"],"first_row_in_archive_order":{"model":"Forte","paper_title":"Forte : Finding Outliers with Representation Typicality Estimation","paper_url":"/paper/forte-finding-outliers-with-representation","paper_date":"2024-10-02","arxiv_id":"2410.01322","code_links":[{"title":"DebarghaG/forte","url":"https://github.com/DebarghaG/forte"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-ade-ood","slug":"out-of-distribution-detection-on-ade-ood","dataset":"ADE-OoD","dataset_url":"/dataset/ade-ood","rows_in_archive":4,"metrics":["AP","FPR@95"],"first_row_in_archive_order":{"model":"RbA","paper_title":"RbA: Segmenting Unknown Regions Rejected by All","paper_url":"/paper/pixels-together-strong-segmenting-unknown","paper_date":"2022-11-25","arxiv_id":"2211.14293","code_links":[{"title":"NazirNayal8/RbA","url":"https://github.com/NazirNayal8/RbA"}],"syntology":{"n":4,"n_ran":0,"n_unverified":4,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100","slug":"out-of-distribution-detection-on-cifar-100","dataset":"CIFAR-100","dataset_url":"/dataset/cifar-100","rows_in_archive":4,"metrics":["FPR95","AUROC"],"first_row_in_archive_order":{"model":"Wide ResNet 40x2","paper_title":"An Effective Baseline for Robustness to Distributional Shift","paper_url":"/paper/an-effective-baseline-for-robustness-to","paper_date":"2021-05-15","arxiv_id":"2105.07107","code_links":[{"title":"Sushil-Thapa/Abstention-OoD","url":"https://github.com/Sushil-Thapa/Abstention-OoD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-ms-1m-vs-ijb","slug":"out-of-distribution-detection-on-ms-1m-vs-ijb","dataset":"MS-1M vs. IJB-C","dataset_url":null,"rows_in_archive":4,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"ResNeXt50 + FSSD","paper_title":"Feature Space Singularity for Out-of-Distribution Detection","paper_url":"/paper/feature-space-singularity-for-out-of","paper_date":"2020-11-30","arxiv_id":"2011.14654","code_links":[{"title":"megvii-research/FSSD_OoD_Detection","url":"https://github.com/megvii-research/FSSD_OoD_Detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-2","slug":"out-of-distribution-detection-on-cifar-10-vs-2","dataset":"CIFAR-10 vs SVHN","dataset_url":null,"rows_in_archive":3,"metrics":["AUROC","FPR95"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet","slug":"out-of-distribution-detection-on-imagenet","dataset":"ImageNet dogs vs ImageNet non-dogs","dataset_url":null,"rows_in_archive":3,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"ResNet34 + FSSD","paper_title":"Feature Space Singularity for Out-of-Distribution Detection","paper_url":"/paper/feature-space-singularity-for-out-of","paper_date":"2020-11-30","arxiv_id":"2011.14654","code_links":[{"title":"megvii-research/FSSD_OoD_Detection","url":"https://github.com/megvii-research/FSSD_OoD_Detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-1","slug":"out-of-distribution-detection-on-imagenet-1k-1","dataset":"ImageNet-1K vs ImageNet-O","dataset_url":null,"rows_in_archive":3,"metrics":["FPR95","AUROC"],"first_row_in_archive_order":{"model":"NNGuide-ViM (ViT-B/16)","paper_title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","paper_url":"/paper/nearest-neighbor-guidance-for-out-of-1","paper_date":"2023-09-26","arxiv_id":"2309.14888","code_links":[{"title":"jingkang50/openood","url":"https://github.com/jingkang50/openood"},{"title":"roomo7time/nnguide","url":"https://github.com/roomo7time/nnguide"}],"syntology":{"n":8,"n_ran":6,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-20","slug":"out-of-distribution-detection-on-20","dataset":"20 Newsgroups","dataset_url":"/dataset/20-newsgroups","rows_in_archive":2,"metrics":["AUROC","FPR95"],"first_row_in_archive_order":{"model":"2-Layered GRU","paper_title":"An Effective Baseline for Robustness to Distributional Shift","paper_url":"/paper/an-effective-baseline-for-robustness-to","paper_date":"2021-05-15","arxiv_id":"2105.07107","code_links":[{"title":"Sushil-Thapa/Abstention-OoD","url":"https://github.com/Sushil-Thapa/Abstention-OoD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-3","slug":"out-of-distribution-detection-on-cifar-10-vs-3","dataset":"CIFAR-10 vs LSUN (C)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-4","slug":"out-of-distribution-detection-on-cifar-10-vs-4","dataset":"CIFAR-10 vs iSUN","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-5","slug":"out-of-distribution-detection-on-cifar-10-vs-5","dataset":"CIFAR-10 vs ImageNet (R)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-6","slug":"out-of-distribution-detection-on-cifar-10-vs-6","dataset":"CIFAR-10 vs ImageNet (C)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-7","slug":"out-of-distribution-detection-on-cifar-10-vs-7","dataset":"CIFAR-10 vs Uniform","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-8","slug":"out-of-distribution-detection-on-cifar-10-vs-8","dataset":"CIFAR-10 vs Gaussian","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-9","slug":"out-of-distribution-detection-on-cifar-10-vs-9","dataset":"CIFAR-10 vs LSUN (R)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"ResNet-34","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-1","slug":"out-of-distribution-detection-on-cifar-100-vs-1","dataset":"CIFAR-100 vs iSUN","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-2","slug":"out-of-distribution-detection-on-cifar-100-vs-2","dataset":"CIFAR-100 vs LSUN (C)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"ResNet-34","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-3","slug":"out-of-distribution-detection-on-cifar-100-vs-3","dataset":"CIFAR-100 vs LSUN (R)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-4","slug":"out-of-distribution-detection-on-cifar-100-vs-4","dataset":"CIFAR-100 vs ImageNet (R)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-5","slug":"out-of-distribution-detection-on-cifar-100-vs-5","dataset":"CIFAR-100 vs ImageNet (C)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-6","slug":"out-of-distribution-detection-on-cifar-100-vs-6","dataset":"CIFAR-100 vs Uniform","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-100-vs-7","slug":"out-of-distribution-detection-on-cifar-100-vs-7","dataset":"CIFAR-100 vs Gaussian","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-far-ood","slug":"out-of-distribution-detection-on-far-ood","dataset":"Far-OOD","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC","FPR@95","ID ACC"],"first_row_in_archive_order":{"model":"ISH (ResNet50)","paper_title":"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement","paper_url":"/paper/scaling-for-training-time-and-post-hoc-out-of","paper_date":"2023-09-30","arxiv_id":"2310.00227","code_links":[{"title":"kai422/scale","url":"https://github.com/kai422/scale"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-fashion","slug":"out-of-distribution-detection-on-fashion","dataset":"Fashion-MNIST","dataset_url":"/dataset/fashion-mnist","rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"PAE","paper_title":"Probabilistic Autoencoder","paper_url":"/paper/probabilistic-auto-encoder","paper_date":"2020-06-09","arxiv_id":"2006.05479","code_links":[{"title":"VMBoehm/PAE","url":"https://github.com/VMBoehm/PAE"},{"title":"chrvt/denoising-normalizing-flow","url":"https://github.com/chrvt/denoising-normalizing-flow"},{"title":"AI-for-Ocean-Science/ulmo","url":"https://github.com/AI-for-Ocean-Science/ulmo"},{"title":"vmboehm/pae-ablation","url":"https://github.com/vmboehm/pae-ablation"}],"syntology":{"n":13,"n_ran":1,"n_unverified":12,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-near-ood","slug":"out-of-distribution-detection-on-near-ood","dataset":"Near-OOD","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC","FPR@95","ID ACC"],"first_row_in_archive_order":{"model":"ISH (ResNet50)","paper_title":"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement","paper_url":"/paper/scaling-for-training-time-and-post-hoc-out-of","paper_date":"2023-09-30","arxiv_id":"2310.00227","code_links":[{"title":"kai422/scale","url":"https://github.com/kai422/scale"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs","slug":"out-of-distribution-detection-on-svhn-vs","dataset":"SVHN vs ImageNet (R)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-1","slug":"out-of-distribution-detection-on-svhn-vs-1","dataset":"SVHN vs ImageNet (C)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-2","slug":"out-of-distribution-detection-on-svhn-vs-2","dataset":"SVHN vs Uniform","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-3","slug":"out-of-distribution-detection-on-svhn-vs-3","dataset":"SVHN vs Gaussian","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-4","slug":"out-of-distribution-detection-on-svhn-vs-4","dataset":"SVHN vs CIFAR-10","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-5","slug":"out-of-distribution-detection-on-svhn-vs-5","dataset":"SVHN vs CIFAR-100","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-isun","slug":"out-of-distribution-detection-on-svhn-vs-isun","dataset":"SVHN vs iSUN","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-lsun","slug":"out-of-distribution-detection-on-svhn-vs-lsun","dataset":"SVHN vs LSUN (C)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-svhn-vs-lsun-1","slug":"out-of-distribution-detection-on-svhn-vs-lsun-1","dataset":"SVHN vs LSUN (R)","dataset_url":null,"rows_in_archive":2,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"DenseNet-BC-100","paper_title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","paper_url":"/paper/single-layer-predictive-normalized-maximum","paper_date":"2021-10-18","arxiv_id":"2110.09246","code_links":[{"title":"kobybibas/pnml_ood_detection","url":"https://github.com/kobybibas/pnml_ood_detection"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar-10-vs-10","slug":"out-of-distribution-detection-on-cifar-10-vs-10","dataset":"CIFAR-10 vs CIFAR-10.1","dataset_url":null,"rows_in_archive":1,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"ERD (ResNet18)","paper_title":"Semi-supervised novelty detection using ensembles with regularized disagreement","paper_url":"/paper/learn-what-you-can-t-learn-regularized-1","paper_date":"2020-12-10","arxiv_id":"2012.05825","code_links":[{"title":"ericpts/reto","url":"https://github.com/ericpts/reto"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar10","slug":"out-of-distribution-detection-on-cifar10","dataset":"CIFAR10","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"Wide ResNet 40x2","paper_title":"RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection","paper_url":"/paper/rodd-a-self-supervised-approach-for-robust","paper_date":"2022-04-06","arxiv_id":"2204.02553","code_links":[{"title":"UmarKhalidcs/RODD","url":"https://github.com/UmarKhalidcs/RODD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar10-1","slug":"out-of-distribution-detection-on-cifar10-1","dataset":"cifar10","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"Wideresnet 40","paper_title":"RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection","paper_url":"/paper/rodd-a-self-supervised-approach-for-robust","paper_date":"2022-04-06","arxiv_id":"2204.02553","code_links":[{"title":"UmarKhalidcs/RODD","url":"https://github.com/UmarKhalidcs/RODD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-cifar100","slug":"out-of-distribution-detection-on-cifar100","dataset":"cifar100","dataset_url":"/dataset/cifar-100","rows_in_archive":1,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"Wide Resnet  40x2","paper_title":"RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection","paper_url":"/paper/rodd-a-self-supervised-approach-for-robust","paper_date":"2022-04-06","arxiv_id":"2204.02553","code_links":[{"title":"UmarKhalidcs/RODD","url":"https://github.com/UmarKhalidcs/RODD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-14","slug":"out-of-distribution-detection-on-imagenet-1k-14","dataset":"ImageNet-1K vs ImageNet-C","dataset_url":null,"rows_in_archive":1,"metrics":["AUROC","FPR95","Latency, ms"],"first_row_in_archive_order":{"model":"DisCoPatch","paper_title":"DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection","paper_url":"/paper/discopatch-batch-statistics-are-all-you-need","paper_date":"2025-01-14","arxiv_id":"2501.08005","code_links":[],"syntology":{"n":6,"n_ran":3,"n_unverified":3,"n_pointer_only":6}}},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-15","slug":"out-of-distribution-detection-on-imagenet-1k-15","dataset":"ImageNet-1K vs SSB-hard","dataset_url":null,"rows_in_archive":1,"metrics":["AUROC","FPR95","Latency, ms"],"first_row_in_archive_order":{"model":"DisCoPatch","paper_title":"DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection","paper_url":"/paper/discopatch-batch-statistics-are-all-you-need","paper_date":"2025-01-14","arxiv_id":"2501.08005","code_links":[],"syntology":{"n":6,"n_ran":3,"n_unverified":3,"n_pointer_only":6}}},{"leaderboard":"/sota/out-of-distribution-detection-on-sst","slug":"out-of-distribution-detection-on-sst","dataset":"SST","dataset_url":"/dataset/sst","rows_in_archive":1,"metrics":["AUROC","FPR95"],"first_row_in_archive_order":{"model":"2-Layered GRU","paper_title":"An Effective Baseline for Robustness to Distributional Shift","paper_url":"/paper/an-effective-baseline-for-robustness-to","paper_date":"2021-05-15","arxiv_id":"2105.07107","code_links":[{"title":"Sushil-Thapa/Abstention-OoD","url":"https://github.com/Sushil-Thapa/Abstention-OoD"}],"syntology":null}},{"leaderboard":"/sota/out-of-distribution-detection-on-trec-news","slug":"out-of-distribution-detection-on-trec-news","dataset":"TREC-NEWS","dataset_url":"/dataset/trec-news-1","rows_in_archive":1,"metrics":["AUROC","FPR95"],"first_row_in_archive_order":{"model":"2-Layered GRU","paper_title":"An Effective Baseline for Robustness to Distributional Shift","paper_url":"/paper/an-effective-baseline-for-robustness-to","paper_date":"2021-05-15","arxiv_id":"2105.07107","code_links":[{"title":"Sushil-Thapa/Abstention-OoD","url":"https://github.com/Sushil-Thapa/Abstention-OoD"}],"syntology":null}}],"datasets":[{"url":"/dataset/cifar-10","name":"CIFAR-10","full_name":"CIFAR-10","num_papers_in_archive":16145},{"url":"/dataset/cifar-100","name":"CIFAR-100","full_name":"","num_papers_in_archive":9045},{"url":"/dataset/fashion-mnist","name":"Fashion-MNIST","full_name":"","num_papers_in_archive":3202},{"url":"/dataset/sst","name":"SST","full_name":"Stanford Sentiment Treebank","num_papers_in_archive":2354},{"url":"/dataset/stl-10","name":"STL-10","full_name":"Self-Taught Learning 10","num_papers_in_archive":1092},{"url":"/dataset/isun","name":"iSUN","full_name":"iSUN","num_papers_in_archive":108},{"url":"/dataset/places365","name":"Places365","full_name":"","num_papers_in_archive":65},{"url":"/dataset/imagenet-1k-vs-openimage-o","name":"OpenImage-O","full_name":"","num_papers_in_archive":29},{"url":"/dataset/20-newsgroups","name":"20 Newsgroups","full_name":"","num_papers_in_archive":27},{"url":"/dataset/imagenet-1k-vs-textures","name":"ImageNet-1k vs Textures","full_name":"","num_papers_in_archive":27},{"url":"/dataset/imagenet-1k-vs-inaturalist-1","name":"ImageNet-1k vs iNaturalist","full_name":"","num_papers_in_archive":26},{"url":"/dataset/imagenet-1k-vs-places","name":"ImageNet-1k vs Places","full_name":"","num_papers_in_archive":21},{"url":"/dataset/imagenet-1k-vs-sun","name":"ImageNet-1k vs SUN","full_name":"","num_papers_in_archive":19},{"url":"/dataset/ninco","name":"NINCO","full_name":"No ImageNet Class Objects","num_papers_in_archive":17},{"url":"/dataset/covid-19-twitter-chatter-dataset","name":"COVID-19 Twitter Chatter Dataset","full_name":"","num_papers_in_archive":10},{"url":"/dataset/imagenet-k-vs-openimage-o","name":"ImageNet-1k vs OpenImage-O","full_name":"","num_papers_in_archive":6},{"url":"/dataset/ade-ood","name":"ADE-OoD","full_name":"","num_papers_in_archive":4},{"url":"/dataset/imagenet-1k-vs-ninco","name":"ImageNet-1k vs NINCO","full_name":"No ImageNet Class Objects","num_papers_in_archive":4},{"url":"/dataset/icons-50","name":"Icons-50","full_name":"","num_papers_in_archive":2},{"url":"/dataset/multiood","name":"MultiOOD","full_name":"Multimodal Out-of-Distribution Detection Benchmark","num_papers_in_archive":2},{"url":"/dataset/pano3d","name":"Pano3D","full_name":"","num_papers_in_archive":2},{"url":"/dataset/imagenet-c-ood-class-out-of-distribution","name":"ImageNet C-OOD (class-out-of-distribution)","full_name":"","num_papers_in_archive":1},{"url":"/dataset/real-bacteria-dataset","name":"Real Bacteria Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/simulated-micro-doppler-signatures","name":"Simulated micro-Doppler Signatures","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":438,"tagged_in_all":888,"items":[{"url":"/paper/cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","arxiv_id":"1905.04899","repositories_listed":30,"syntology":{"n":24,"n_ran":17,"n_unverified":7,"n_pointer_only":5}},{"url":"/paper/a-baseline-for-detecting-misclassified-and","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","date":"2016-10-07","arxiv_id":"1610.02136","repositories_listed":14,"syntology":{"n":21,"n_ran":7,"n_unverified":14,"n_pointer_only":6}},{"url":"/paper/deep-anomaly-detection-with-outlier-exposure","title":"Deep Anomaly Detection with Outlier Exposure","date":"2018-12-11","arxiv_id":"1812.04606","repositories_listed":9,"syntology":{"n":5,"n_ran":5,"n_unverified":0,"n_pointer_only":4}},{"url":"/paper/enhancing-the-reliability-of-out-of","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","date":"2017-06-08","arxiv_id":"1706.02690","repositories_listed":9,"syntology":{"n":3,"n_ran":2,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/energy-based-out-of-distribution-detection-1","title":"Energy-based Out-of-distribution Detection","date":"2020-10-08","arxiv_id":"2010.03759","repositories_listed":6,"syntology":{"n":6,"n_ran":3,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/mos-towards-scaling-out-of-distribution","title":"MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space","date":"2021-05-05","arxiv_id":"2105.01879","repositories_listed":5,"syntology":{"n":4,"n_ran":3,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/detecting-out-of-distribution-examples-with","title":"Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices","date":"2019-12-28","arxiv_id":"1912.12510","repositories_listed":5,"syntology":{"n":8,"n_ran":5,"n_unverified":3,"n_pointer_only":8}},{"url":"/paper/learning-confidence-for-out-of-distribution","title":"Learning Confidence for Out-of-Distribution Detection in Neural Networks","date":"2018-02-13","arxiv_id":"1802.04865","repositories_listed":5,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/zero-shot-in-distribution-detection-in-multi","title":"GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection","date":"2023-04-10","arxiv_id":"2304.04521","repositories_listed":4,"syntology":{"n":16,"n_ran":5,"n_unverified":11,"n_pointer_only":0}},{"url":"/paper/openood-benchmarking-generalized-out-of","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection","date":"2022-10-13","arxiv_id":"2210.07242","repositories_listed":4,"syntology":{"n":17,"n_ran":8,"n_unverified":9,"n_pointer_only":0}},{"url":"/paper/generalized-out-of-distribution-detection-a","title":"Generalized Out-of-Distribution Detection: A Survey","date":"2021-10-21","arxiv_id":"2110.11334","repositories_listed":4,"syntology":null},{"url":"/paper/a-gentle-introduction-to-conformal-prediction","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","date":"2021-07-15","arxiv_id":"2107.07511","repositories_listed":4,"syntology":{"n":12,"n_ran":0,"n_unverified":12,"n_pointer_only":0}},{"url":"/paper/open-set-label-noise-can-improve-robustness","title":"Open-set Label Noise Can Improve Robustness Against Inherent Label Noise","date":"2021-06-21","arxiv_id":"2106.10891","repositories_listed":4,"syntology":{"n":29,"n_ran":17,"n_unverified":12,"n_pointer_only":29}},{"url":"/paper/a-simple-fix-to-mahalanobis-distance-for","title":"A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection","date":"2021-06-16","arxiv_id":"2106.09022","repositories_listed":4,"syntology":null},{"url":"/paper/hierarchical-vaes-know-what-they-don-t-know","title":"Hierarchical VAEs Know What They Don't Know","date":"2021-02-16","arxiv_id":"2102.08248","repositories_listed":4,"syntology":{"n":10,"n_ran":8,"n_unverified":2,"n_pointer_only":9}},{"url":"/paper/masksembles-for-uncertainty-estimation","title":"Masksembles for Uncertainty Estimation","date":"2020-12-15","arxiv_id":"2012.08334","repositories_listed":4,"syntology":null},{"url":"/paper/probabilistic-auto-encoder","title":"Probabilistic Autoencoder","date":"2020-06-09","arxiv_id":"2006.05479","repositories_listed":4,"syntology":{"n":13,"n_ran":1,"n_unverified":12,"n_pointer_only":0}},{"url":"/paper/a-benchmark-for-anomaly-segmentation","title":"Scaling Out-of-Distribution Detection for Real-World Settings","date":"2019-11-25","arxiv_id":"1911.11132","repositories_listed":4,"syntology":{"n":4,"n_ran":3,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/using-self-supervised-learning-can-improve","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","date":"2019-06-28","arxiv_id":"1906.12340","repositories_listed":4,"syntology":{"n":12,"n_ran":5,"n_unverified":7,"n_pointer_only":0}},{"url":"/paper/likelihood-ratios-for-out-of-distribution","title":"Likelihood Ratios for Out-of-Distribution Detection","date":"2019-06-07","arxiv_id":"1906.02845","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/a-simple-unified-framework-for-detecting-out","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","date":"2018-07-10","arxiv_id":"1807.03888","repositories_listed":4,"syntology":{"n":13,"n_ran":9,"n_unverified":4,"n_pointer_only":12}},{"url":"/paper/probabilistic-mimo-u-net-efficient-and","title":"Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression","date":"2023-08-14","arxiv_id":"2308.07477","repositories_listed":3,"syntology":null},{"url":"/paper/openood-v1-5-enhanced-benchmark-for-out-of","title":"OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection","date":"2023-06-15","arxiv_id":"2306.09301","repositories_listed":3,"syntology":null},{"url":"/paper/gmmseg-gaussian-mixture-based-generative","title":"GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models","date":"2022-10-05","arxiv_id":"2210.02025","repositories_listed":3,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/out-of-distribution-detection-via-neural","title":"Out of Distribution Detection via Neural Network Anchoring","date":"2022-07-08","arxiv_id":"2207.04125","repositories_listed":3,"syntology":null},{"url":"/paper/out-of-distribution-detection-with-deep","title":"Out-of-Distribution Detection with Deep Nearest Neighbors","date":"2022-04-13","arxiv_id":"2204.06507","repositories_listed":3,"syntology":{"n":38,"n_ran":30,"n_unverified":8,"n_pointer_only":38}},{"url":"/paper/self-supervised-out-of-distribution-detection-1","title":"Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization","date":"2021-09-30","arxiv_id":"2109.15222","repositories_listed":3,"syntology":{"n":8,"n_ran":2,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/ssd-a-unified-framework-for-self-supervised-1","title":"SSD: A Unified Framework for Self-Supervised Outlier Detection","date":"2021-03-22","arxiv_id":"2103.12051","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/a-benchmark-of-medical-out-of-distribution","title":"A Benchmark of Medical Out of Distribution Detection","date":"2020-07-08","arxiv_id":"2007.04250","repositories_listed":3,"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/exact-information-bottleneck-with-invertible","title":"Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification","date":"2020-01-17","arxiv_id":"2001.06448","repositories_listed":3,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}],"syntology_records":24,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}