{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/out-of-distribution-detection/papers/8","list_of":"/task/out-of-distribution-detection","task":"Out-of-Distribution Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":9,"rows_per_page":100,"rows":[701,800],"of":888,"counts":{"archive_papers_tagged":888,"with_a_code_link":438,"where_syntology_ran_a_sample":181,"not_listed_spam_title":0,"listed":888,"listed_where_code_ran":181,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":146,"every_run_a_failure_of_syntologys_instrument":35,"listed_with_a_run_with_no_instrument_failure":146,"listed_every_run_a_failure_of_syntologys_instrument":35,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/out-of-distribution-detection","prev":"/task/out-of-distribution-detection/papers/7","next":"/task/out-of-distribution-detection/papers/9","papers":[{"url":null,"slug":"probabilistic-safe-online-learning-with","title":"Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions","date":"2022-08-23","arxiv_id":"2208.10733","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-driven-energy-based-out-of","title":"Semantic Driven Energy based Out-of-Distribution Detection","date":"2022-08-23","arxiv_id":"2208.10787","repositories_listed":0,"syntology":null},{"url":"/paper/outlier-detection-using-self-organizing-maps","slug":"outlier-detection-using-self-organizing-maps","title":"Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis","date":"2022-08-18","arxiv_id":"2208.08834","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/outlier-detection-using-self-organizing-maps#ran","syntology_url":"https://syntology.ai/paper/2208.08834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08834"}},"official":null}},{"url":null,"slug":"deep-unsupervised-domain-adaptation-a-review","title":"Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives","date":"2022-08-15","arxiv_id":"2208.07422","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-knowledge-distillation-via-dropout","title":"Self-Knowledge Distillation via Dropout","date":"2022-08-11","arxiv_id":"2208.05642","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-agnostic-continual-hippocampus","title":"Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts","date":"2022-08-05","arxiv_id":"2208.03206","repositories_listed":0,"syntology":null},{"url":null,"slug":"p-dknn-out-of-distribution-detection-through","title":"$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations","date":"2022-07-25","arxiv_id":"2207.12545","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-test-time-method-for-out-of","title":"A Simple Test-Time Method for Out-of-Distribution Detection","date":"2022-07-17","arxiv_id":"2207.08210","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-inlier-and-outlier-specification","title":"Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors","date":"2022-07-12","arxiv_id":"2207.05286","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributional-gaussian-processes-layers-for","title":"Distributional Gaussian Processes Layers for Out-of-Distribution Detection","date":"2022-06-27","arxiv_id":"2206.13346","repositories_listed":0,"syntology":null},{"url":null,"slug":"batch-ensemble-stochastic-neural-networks-for","title":"Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection","date":"2022-06-26","arxiv_id":"2206.12911","repositories_listed":0,"syntology":null},{"url":null,"slug":"weshort-out-of-distribution-detection-with","title":"WeShort: Out-of-distribution Detection With Weak Shortcut structure","date":"2022-06-23","arxiv_id":"2207.05055","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-ood-detection-in-graph-classification","title":"Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective","date":"2022-06-21","arxiv_id":"2206.10691","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-out-of-distribution","title":"Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space","date":"2022-06-20","arxiv_id":"2206.09543","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-testing-framework-for-out-of","title":"Multiple Testing Framework for Out-of-Distribution Detection","date":"2022-06-20","arxiv_id":"2206.09522","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervision-adaptation-balances-in","title":"Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection","date":"2022-06-19","arxiv_id":"2206.09380","repositories_listed":0,"syntology":null},{"url":null,"slug":"reduced-robust-random-cut-forest-for-out-of","title":"Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models","date":"2022-06-18","arxiv_id":"2206.09247","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-based-adversarial-and-out-of","title":"Gradient-Based Adversarial and Out-of-Distribution Detection","date":"2022-06-16","arxiv_id":"2206.08255","repositories_listed":0,"syntology":null},{"url":null,"slug":"read-aggregating-reconstruction-error-into","title":"READ: Aggregating Reconstruction Error into Out-of-distribution Detection","date":"2022-06-15","arxiv_id":"2206.07459","repositories_listed":0,"syntology":null},{"url":null,"slug":"energymatch-energy-based-pseudo-labeling-for","title":"EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning","date":"2022-06-13","arxiv_id":"2206.06359","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-with-class","title":"Out-of-Distribution Detection with Class Ratio Estimation","date":"2022-06-08","arxiv_id":"2206.03955","repositories_listed":0,"syntology":null},{"url":null,"slug":"which-models-are-innately-best-at-uncertainty","title":"Which models are innately best at uncertainty estimation?","date":"2022-06-05","arxiv_id":"2206.02152","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-using-bigan-and","title":"Out-of-Distribution Detection using BiGAN and MDL","date":"2022-06-03","arxiv_id":"2206.01851","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-bayesian-neural-subnetwork","title":"Sequential Bayesian Neural Subnetwork Ensembles","date":"2022-06-01","arxiv_id":"2206.00794","repositories_listed":0,"syntology":null},{"url":null,"slug":"norm-scaling-for-out-of-distribution","title":"Norm-Scaling for Out-of-Distribution Detection","date":"2022-05-06","arxiv_id":"2205.03493","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-of-out-of-distribution-1","title":"Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks","date":"2022-04-26","arxiv_id":"2204.12378","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-by-erasing-conditional-entropy-based","title":"Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection","date":"2022-04-23","arxiv_id":"2204.11041","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-in-unsupervised","title":"Out-Of-Distribution Detection In Unsupervised Continual Learning","date":"2022-04-12","arxiv_id":"2204.05462","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-out-of-distribution-detection-in","title":"Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss","date":"2022-04-10","arxiv_id":"2204.04665","repositories_listed":0,"syntology":null},{"url":null,"slug":"transgan-a-transductive-adversarial-model-for","title":"TransductGAN: a Transductive Adversarial Model for Novelty Detection","date":"2022-03-29","arxiv_id":"2203.15406","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-level-doubly-variational-learning-for","title":"Bi-level Doubly Variational Learning for Energy-based Latent Variable Models","date":"2022-03-24","arxiv_id":"2203.14702","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-generalization","title":"Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem","date":"2022-03-23","arxiv_id":"2203.12145","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-it-all-a-cluster-game-exploring-out-of","title":"Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space","date":"2022-03-16","arxiv_id":"2203.08549","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-distribution-distillation-efficient-1","title":"Self-Distribution Distillation: Efficient Uncertainty Estimation","date":"2022-03-15","arxiv_id":"2203.08295","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-out-of-distribution-detection","title":"Model-agnostic out-of-distribution detection using combined statistical tests","date":"2022-03-02","arxiv_id":"2203.01097","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-randomness-in-evaluation-protocols","title":"Addressing Randomness in Evaluation Protocols for Out-of-Distribution Detection","date":"2022-03-01","arxiv_id":"2203.00382","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-dirichlet-uncertainty-for-unsupervised","title":"Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening","date":"2022-02-25","arxiv_id":"2202.12634","repositories_listed":0,"syntology":null},{"url":null,"slug":"computer-aided-diagnosis-and-out-of","title":"Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography","date":"2022-02-24","arxiv_id":"2202.11944","repositories_listed":0,"syntology":null},{"url":null,"slug":"model2detector-widening-the-information","title":"Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps","date":"2022-02-22","arxiv_id":"2202.11226","repositories_listed":0,"syntology":null},{"url":"/paper/the-met-dataset-instance-level-recognition","slug":"the-met-dataset-instance-level-recognition","title":"The Met Dataset: Instance-level Recognition for Artworks","date":"2022-02-03","arxiv_id":"2202.01747","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-robustness-and-calibration-in","title":"Improving robustness and calibration in ensembles with diversity regularization","date":"2022-01-26","arxiv_id":"2201.10908","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-enforced-transfer-a-novel-domain","title":"E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing","date":"2022-01-24","arxiv_id":"2201.10001","repositories_listed":0,"syntology":null},{"url":"/paper/idecode-in-distribution-equivariance-for","slug":"idecode-in-distribution-equivariance-for","title":"iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection","date":"2022-01-07","arxiv_id":"2201.02331","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/idecode-in-distribution-equivariance-for#ran","syntology_url":"https://syntology.ai/paper/2201.02331","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.02331"}},"official":null}},{"url":"/paper/deep-hybrid-models-for-out-of-distribution","slug":"deep-hybrid-models-for-out-of-distribution","title":"Deep Hybrid Models for Out-of-Distribution Detection","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dense-anomaly-detection-by-robust-learning-on","slug":"dense-anomaly-detection-by-robust-learning-on","title":"Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data","date":"2021-12-23","arxiv_id":"2112.12833","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-aware-learning-for-out-of","title":"Boundary Aware Learning for Out-of-distribution Detection","date":"2021-12-22","arxiv_id":"2112.11648","repositories_listed":0,"syntology":null},{"url":"/paper/out-of-distribution-detection-without-class","slug":"out-of-distribution-detection-without-class","title":"Out-of-Distribution Detection Without Class Labels","date":"2021-12-14","arxiv_id":"2112.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-visual-self-supervision-and-its-effect-on","title":"On visual self-supervision and its effect on model robustness","date":"2021-12-08","arxiv_id":"2112.04367","repositories_listed":0,"syntology":null},{"url":null,"slug":"extrapolation-frameworks-in-cognitive","title":"Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models","date":"2021-12-06","arxiv_id":"2112.03411","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmark-for-out-of-distribution-detection","title":"Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning","date":"2021-12-05","arxiv_id":"2112.02694","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-most-powerful-bayesian-test-for-out","title":"Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"step-out-of-distribution-detection-in-the","title":"STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"frob-few-shot-robust-model-for-classification-1","title":"FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection","date":"2021-11-30","arxiv_id":"2111.15487","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-invariants-to-understand-unsupervised","title":"Data Invariants to Understand Unsupervised Out-of-Distribution Detection","date":"2021-11-26","arxiv_id":"2111.13362","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-deep-representation-learning-based","title":"Deep Representation Learning with an Information-theoretic Loss","date":"2021-11-25","arxiv_id":"2111.12950","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-anomaly-detection-using-self","title":"Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks","date":"2021-11-24","arxiv_id":"2111.12379","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-perspectives-on-reliability-of","title":"Statistical Perspectives on Reliability of Artificial Intelligence Systems","date":"2021-11-09","arxiv_id":"2111.05391","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-for-skin-and","title":"Out of distribution detection for skin and malaria images","date":"2021-11-02","arxiv_id":"2111.01505","repositories_listed":0,"syntology":null},{"url":null,"slug":"kfolden-k-fold-ensemble-for-out-of","title":"kFolden: k-Fold Ensemble for Out-Of-Distribution Detection","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"validate-on-sim-detect-on-real-model","title":"Validate on Sim, Detect on Real -- Model Selection for Domain Randomization","date":"2021-11-01","arxiv_id":"2111.00765","repositories_listed":0,"syntology":null},{"url":null,"slug":"pnpood-out-of-distribution-detection-for-text","title":"PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation","date":"2021-10-31","arxiv_id":"2111.00506","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-wise-thresholding-for-detecting-out-of","title":"Class-wise Thresholding for Robust Out-of-Distribution Detection","date":"2021-10-28","arxiv_id":"2110.15292","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-and-trustworthy-machine-learning-for","title":"Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection","date":"2021-10-26","arxiv_id":"2110.14019","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-role-of-self-supervised","title":"Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task","date":"2021-10-26","arxiv_id":"2110.13435","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-out-of-distribution-detection-in-cnns","title":"Why Out-of-distribution Detection in CNNs Does Not Like Mahalanobis -- and What to Use Instead","date":"2021-10-13","arxiv_id":"2110.07043","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-classifiers-with-label-noise-modeling","title":"Deep Classifiers with Label Noise Modeling and Distance Awareness","date":"2021-10-06","arxiv_id":"2110.02609","repositories_listed":0,"syntology":null},{"url":null,"slug":"d-uq-accurate-uncertainty-quantification-via","title":"$Δ$-UQ: Accurate Uncertainty Quantification via Anchor Marginalization","date":"2021-10-05","arxiv_id":"2110.02197","repositories_listed":0,"syntology":null},{"url":null,"slug":"decomposing-texture-and-semantics-for-out-of","title":"Decomposing Texture and Semantics for Out-of-distribution Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dice-a-simple-sparsification-method-for-out","title":"DICE: A Simple Sparsification Method for Out-of-distribution Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-out-of-distribution-detection-via","title":"Efficient Out-of-Distribution Detection via CVAE data Generation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"frob-few-shot-robust-model-for-classification","title":"FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-and-assessing-anomaly-detectors-for","title":"Improving and Assessing Anomaly Detectors for Large-Scale Settings","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"intra-class-mixup-for-out-of-distribution","title":"Intra-class Mixup for Out-of-Distribution Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-flow-generative-models-for-out-of","title":"Revisiting flow generative models for Out-of-distribution detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-out-of-distribution-detection-a","title":"Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-joint-supervised-learning","title":"Self-Joint Supervised Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sneakoscope-revisiting-unsupervised-out-of","title":"Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-needle-in-the-haystack-out-distribution","title":"The Needle in the haystack: Out-distribution aware Self-training in an Open-World Setting","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-robustness-and-efficiency-in-active","title":"Robust Contrastive Active Learning with Feature-guided Query Strategies","date":"2021-09-13","arxiv_id":"2109.06873","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-out-of-distribution-detection-using","title":"Efficient Out-of-Distribution Detection Using Latent Space of $β$-VAE for Cyber-Physical Systems","date":"2021-08-26","arxiv_id":"2108.11800","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascade-watchdog-a-multi-tiered-adversarial","title":"Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection","date":"2021-08-20","arxiv_id":"2108.09375","repositories_listed":0,"syntology":null},{"url":null,"slug":"teaching-uncertainty-quantification-in","title":"Teaching Uncertainty Quantification in Machine Learning through Use Cases","date":"2021-08-19","arxiv_id":"2108.08712","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-density-estimation-and-uncertainty","title":"Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra","date":"2021-08-17","arxiv_id":"2108.08709","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-estimation-and-out-of","title":"Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions","date":"2021-07-20","arxiv_id":"2107.09734","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-failures-in-out-of-distribution","title":"Understanding Failures in Out-of-Distribution Detection with Deep Generative Models","date":"2021-07-14","arxiv_id":"2107.06908","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-discriminant-latent-space-with","title":"Learning a Discriminant Latent Space with Neural Discriminant Analysis","date":"2021-07-13","arxiv_id":"2107.06209","repositories_listed":0,"syntology":null},{"url":null,"slug":"thinkback-task-specificout-of-distribution","title":"Thinkback: Task-SpecificOut-of-Distribution Detection","date":"2021-07-13","arxiv_id":"2107.06668","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-and-adversarial","title":"Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis","date":"2021-07-10","arxiv_id":"2107.04882","repositories_listed":0,"syntology":null},{"url":null,"slug":"earlin-early-out-of-distribution-detection","title":"EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference","date":"2021-06-25","arxiv_id":"2106.13842","repositories_listed":0,"syntology":null},{"url":"/paper/multi-task-transformation-learning-for-robust","slug":"multi-task-transformation-learning-for-robust","title":"Shifting Transformation Learning for Out-of-Distribution Detection","date":"2021-06-07","arxiv_id":"2106.03899","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-representation-to-rule-them-all","title":"One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations","date":"2021-06-02","arxiv_id":"2106.01423","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-compositionality-of-neural-networks","title":"Improving Compositionality of Neural Networks by Decoding Representations to Inputs","date":"2021-06-01","arxiv_id":"2106.00769","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-in-dermatology","title":"Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning","date":"2021-05-24","arxiv_id":"2105.11160","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-entropic-out-of-distribution-1","title":"Improving Entropic Out-of-Distribution Detection using Isometric Distances and the Minimum Distance Score","date":"2021-05-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"topological-uncertainty-monitoring-trained","title":"Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs","date":"2021-05-07","arxiv_id":"2105.04404","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributional-gaussian-process-layers-for","title":"Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation","date":"2021-04-28","arxiv_id":"2104.13756","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-detection-of-out-of-distribution","title":"Neural Mean Discrepancy for Efficient Out-of-Distribution Detection","date":"2021-04-23","arxiv_id":"2104.11408","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-find-your-lack-of-uncertainty-in-computer","title":"I Find Your Lack of Uncertainty in Computer Vision Disturbing","date":"2021-04-16","arxiv_id":"2104.08188","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-surrogates-for-deep-learning","title":"Uncertainty Surrogates for Deep Learning","date":"2021-04-16","arxiv_id":"2104.08147","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-for-dermoscopic","title":"Out-of-Distribution Detection for Dermoscopic Image Classification","date":"2021-04-15","arxiv_id":"2104.07819","repositories_listed":0,"syntology":null}],"record_sha256":"1cf11629738d84fc3f00aa043b285fdf1db385d389c9d5e9bb495dd1c220c5a0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}