{"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/anomaly-detection/papers/27","list_of":"/task/anomaly-detection","task":"Anomaly 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":27,"pages_in_order":49,"rows_per_page":100,"rows":[2601,2700],"of":4856,"counts":{"archive_papers_tagged":4856,"with_a_code_link":1727,"where_syntology_ran_a_sample":347,"not_listed_spam_title":0,"listed":4856,"listed_where_code_ran":347,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":294,"every_run_a_failure_of_syntologys_instrument":53,"listed_with_a_run_with_no_instrument_failure":294,"listed_every_run_a_failure_of_syntologys_instrument":53,"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/anomaly-detection","prev":"/task/anomaly-detection/papers/26","next":"/task/anomaly-detection/papers/28","papers":[{"url":null,"slug":"toward-multi-class-anomaly-detection","title":"Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference","date":"2024-03-21","arxiv_id":"2403.14213","repositories_listed":0,"syntology":null},{"url":null,"slug":"triple-component-matrix-factorization","title":"Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components","date":"2024-03-21","arxiv_id":"2404.07955","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-layer-wise-detection","title":"Machine Learning-based Layer-wise Detection of Overheating Anomaly in LPBF using Photodiode Data","date":"2024-03-20","arxiv_id":"2403.13861","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-deep-learning-architectures-1","title":"A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection","date":"2024-03-19","arxiv_id":"2403.12864","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-interpretability-of-scores-in","title":"Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine","date":"2024-03-19","arxiv_id":"2403.12672","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-the-anomalies-in-an-ever-changing","title":"Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning","date":"2024-03-19","arxiv_id":"2403.15463","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-jigsaw-conditioned-diffusion-model-for","title":"Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection","date":"2024-03-18","arxiv_id":"2403.12172","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-should-use","title":"Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)","date":"2024-03-18","arxiv_id":"2403.11532","repositories_listed":0,"syntology":null},{"url":null,"slug":"causality-from-bottom-to-top-a-survey","title":"Causality from Bottom to Top: A Survey","date":"2024-03-17","arxiv_id":"2403.11219","repositories_listed":0,"syntology":null},{"url":null,"slug":"tokensome-towards-a-genetic-vision-language","title":"Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping","date":"2024-03-17","arxiv_id":"2403.11073","repositories_listed":0,"syntology":null},{"url":null,"slug":"usfad-based-effective-unknown-attack","title":"usfAD Based Effective Unknown Attack Detection Focused IDS Framework","date":"2024-03-17","arxiv_id":"2403.11180","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-based-on-isolation","title":"Anomaly Detection Based on Isolation Mechanisms: A Survey","date":"2024-03-16","arxiv_id":"2403.10802","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-is-better-than-modification","title":"Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection","date":"2024-03-15","arxiv_id":"2403.10339","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-and-multi-agent-private-active-sensing","title":"Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach","date":"2024-03-15","arxiv_id":"2403.10112","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-by-adapting-a-pre-trained","title":"Anomaly Detection by Adapting a pre-trained Vision Language Model","date":"2024-03-14","arxiv_id":"2403.09493","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-machine-learning-based-security","title":"Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems","date":"2024-03-14","arxiv_id":"2403.09752","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-memories-enhanced-graph","title":"Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality","date":"2024-03-14","arxiv_id":"2403.09039","repositories_listed":0,"syntology":null},{"url":null,"slug":"caformer-rethinking-time-series-analysis-from","title":"Caformer: Rethinking Time Series Analysis from Causal Perspective","date":"2024-03-13","arxiv_id":"2403.08572","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-explanations-justification-and","title":"Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks","date":"2024-03-13","arxiv_id":"2403.08652","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-time-series-classification-for","title":"Supervised Time Series Classification for Anomaly Detection in Subsea Engineering","date":"2024-03-12","arxiv_id":"2403.08013","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-object-throwing-behavior-in","title":"Detection of Object Throwing Behavior in Surveillance Videos","date":"2024-03-11","arxiv_id":"2403.06552","repositories_listed":0,"syntology":null},{"url":null,"slug":"grid-monitoring-and-protection-with","title":"Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies","date":"2024-03-11","arxiv_id":"2403.06942","repositories_listed":0,"syntology":null},{"url":null,"slug":"study-of-the-impact-of-the-big-data-era-on","title":"Study of the Impact of the Big Data Era on Accounting and Auditing","date":"2024-03-11","arxiv_id":"2403.07180","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-guided-variational-image-generation-for","title":"Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation","date":"2024-03-10","arxiv_id":"2403.06247","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-expressive-and-generalizable-motion","title":"Learning Expressive And Generalizable Motion Features For Face Forgery Detection","date":"2024-03-08","arxiv_id":"2403.05172","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-battery-powered-tinyml-systems","title":"Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection","date":"2024-03-08","arxiv_id":"2403.05106","repositories_listed":0,"syntology":null},{"url":null,"slug":"effectiveness-assessment-of-recent-large","title":"Effectiveness Assessment of Recent Large Vision-Language Models","date":"2024-03-07","arxiv_id":"2403.04306","repositories_listed":0,"syntology":null},{"url":null,"slug":"mkf-ads-a-multi-knowledge-fused-anomaly","title":"MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network","date":"2024-03-07","arxiv_id":"2403.04293","repositories_listed":0,"syntology":null},{"url":null,"slug":"signature-isolation-forest","title":"Signature Isolation Forest","date":"2024-03-07","arxiv_id":"2403.04405","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-bayesian-generative-models-for","title":"Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks","date":"2024-03-06","arxiv_id":"2403.03583","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-anomaly-detection-based-on-deep","title":"Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots","date":"2024-03-06","arxiv_id":"2403.03563","repositories_listed":0,"syntology":null},{"url":null,"slug":"portraying-the-need-for-temporal-data-in","title":"Portraying the Need for Temporal Data in Flood Detection via Sentinel-1","date":"2024-03-06","arxiv_id":"2403.03671","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-security-in-federated-learning","title":"Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation","date":"2024-03-05","arxiv_id":"2403.04803","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-deep-autoencoders-for","title":"Towards efficient deep autoencoders for multivariate time series anomaly detection","date":"2024-03-04","arxiv_id":"2403.02429","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-distance-metric-learning-for","title":"Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series","date":"2024-03-04","arxiv_id":"2403.01895","repositories_listed":0,"syntology":null},{"url":null,"slug":"acme-ad-accelerated-model-explanations-for","title":"AcME-AD: Accelerated Model Explanations for Anomaly Detection","date":"2024-03-02","arxiv_id":"2403.01245","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-driven-anomaly-detection-for","title":"Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks","date":"2024-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-techniques-to","title":"Dimensionality reduction techniques to support insider trading detection","date":"2024-03-01","arxiv_id":"2403.00707","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-frequency-bands-on-acoustic","title":"The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model","date":"2024-03-01","arxiv_id":"2403.00379","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-offshore-wind-turbine","title":"Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling","date":"2024-02-29","arxiv_id":"2402.19295","repositories_listed":0,"syntology":null},{"url":null,"slug":"coft-ad-contrastive-fine-tuning-for-few-shot","title":"COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection","date":"2024-02-29","arxiv_id":"2402.18998","repositories_listed":0,"syntology":null},{"url":null,"slug":"objective-and-interpretable-breast-cosmesis","title":"Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model","date":"2024-02-28","arxiv_id":"2402.18362","repositories_listed":0,"syntology":null},{"url":null,"slug":"cggm-a-conditional-graph-generation-model","title":"CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks","date":"2024-02-27","arxiv_id":"2402.17363","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-teacher-student-normality-learning","title":"Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization","date":"2024-02-27","arxiv_id":"2402.17091","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-gan-for-anomaly-detection-a-cutting","title":"Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management","date":"2024-02-25","arxiv_id":"2402.15945","repositories_listed":0,"syntology":null},{"url":null,"slug":"patchflow-leveraging-a-flow-based-model-with","title":"PatchFlow: Leveraging a Flow-Based Model with Patch Features","date":"2024-02-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-representations-meets-deep-unfolding","title":"Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection","date":"2024-02-23","arxiv_id":"2402.15335","repositories_listed":0,"syntology":null},{"url":null,"slug":"reimagining-anomalies-what-if-anomalies-were","title":"Reimagining Anomalies: What If Anomalies Were Normal?","date":"2024-02-22","arxiv_id":"2402.14469","repositories_listed":0,"syntology":null},{"url":null,"slug":"fgad-self-boosted-knowledge-distillation-for","title":"FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework","date":"2024-02-20","arxiv_id":"2402.12761","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-test-for-generated-hypotheses-by","title":"Statistical Test on Diffusion Model-based Anomaly Detection by Selective Inference","date":"2024-02-19","arxiv_id":"2402.11789","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-density-estimation-based-on-spline","title":"Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling","date":"2024-02-18","arxiv_id":"2402.11552","repositories_listed":0,"syntology":null},{"url":null,"slug":"logelectra-self-supervised-anomaly-detection","title":"LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs","date":"2024-02-16","arxiv_id":"2402.10397","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-cooperative-localization-in-uav-systems","title":"Trustworthy UAV Cooperative Localization: Information Analysis of Performance and Security","date":"2024-02-15","arxiv_id":"2402.09810","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-for-forecasting-and","title":"Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review","date":"2024-02-15","arxiv_id":"2402.10350","repositories_listed":0,"syntology":null},{"url":null,"slug":"utilizing-gans-for-fraud-detection-model","title":"Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data","date":"2024-02-15","arxiv_id":"2402.09830","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-and-application-of-transformer-based","title":"Research and application of Transformer based anomaly detection model: A literature review","date":"2024-02-14","arxiv_id":"2402.08975","repositories_listed":0,"syntology":null},{"url":null,"slug":"apalu-a-trainable-adaptive-activation","title":"APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks","date":"2024-02-13","arxiv_id":"2402.08244","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-hidden-energy-anomalies-harnessing","title":"Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities","date":"2024-02-13","arxiv_id":"2402.08742","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-anomaly-detection-in-modern-power","title":"Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach","date":"2024-02-12","arxiv_id":"2402.07884","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-motion-anomaly-detection-via-cross","title":"Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI","date":"2024-02-10","arxiv_id":"2402.06984","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-anomaly-detection-an-adaptation","title":"Advancing Video Anomaly Detection: A Concise Review and a New Dataset","date":"2024-02-07","arxiv_id":"2402.04857","repositories_listed":0,"syntology":null},{"url":null,"slug":"iot-network-traffic-analysis-with-deep","title":"IoT Network Traffic Analysis with Deep Learning","date":"2024-02-06","arxiv_id":"2402.04469","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-test-for-anomaly-detections-by","title":"Statistical Test for Anomaly Detections by Variational Auto-Encoders","date":"2024-02-06","arxiv_id":"2402.03724","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-anomaly-detection-via","title":"Weakly Supervised Anomaly Detection via Knowledge-Data Alignment","date":"2024-02-06","arxiv_id":"2402.03785","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-class-anomaly-detection-through-color-to","title":"One-class anomaly detection through color-to-thermal AI for building envelope inspection","date":"2024-02-05","arxiv_id":"2402.02963","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-normalizing-flows-for-anomaly","title":"Quantum Normalizing Flows for Anomaly Detection","date":"2024-02-05","arxiv_id":"2402.02866","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-time-series-anomaly-state","title":"Understanding Time Series Anomaly State Detection through One-Class Classification","date":"2024-02-03","arxiv_id":"2402.02007","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-indrnnlstm-approach-for-real-time","title":"A hybrid IndRNNLSTM approach for real-time anomaly detection in software-defined networks","date":"2024-02-02","arxiv_id":"2402.05943","repositories_listed":0,"syntology":null},{"url":null,"slug":"develop-end-to-end-anomaly-detection-system","title":"Develop End-to-End Anomaly Detection System","date":"2024-02-01","arxiv_id":"2402.10085","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-student-knowledge-distillation-networks","title":"Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection","date":"2024-02-01","arxiv_id":"2402.00448","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-validation-of-a-deep-learning","title":"Statistical validation of a deep learning algorithm for dental anomaly detection in intraoral radiographs using paired data","date":"2024-02-01","arxiv_id":"2402.14022","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-visual-anomaly-detection","title":"A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect","date":"2024-01-29","arxiv_id":"2401.16402","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-of-particle-orbit-in","title":"Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology","date":"2024-01-28","arxiv_id":"2401.15543","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-guided-knowledge","title":"Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection","date":"2024-01-26","arxiv_id":"2401.15123","repositories_listed":0,"syntology":null},{"url":null,"slug":"scania-component-x-dataset-a-real-world","title":"SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance","date":"2024-01-26","arxiv_id":"2401.15199","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-hyperspectral-anomaly","title":"Towards Robust Hyperspectral Anomaly Detection: Decomposing Background, Anomaly, and Mixed Noise via Convex Optimization","date":"2024-01-26","arxiv_id":"2401.14814","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-conditional-node-update-graph-neural","title":"Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection","date":"2024-01-25","arxiv_id":"2401.13872","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-physics-informed-machine-learning","title":"A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection","date":"2024-01-22","arxiv_id":"2401.11860","repositories_listed":0,"syntology":null},{"url":null,"slug":"unraveling-attacks-in-machine-learning-based","title":"Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them","date":"2024-01-22","arxiv_id":"2401.11723","repositories_listed":0,"syntology":null},{"url":null,"slug":"dacr-distribution-augmented-contrastive","title":"DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection","date":"2024-01-20","arxiv_id":"2401.11271","repositories_listed":0,"syntology":null},{"url":null,"slug":"maediff-masked-autoencoder-enhanced-diffusion","title":"MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images","date":"2024-01-19","arxiv_id":"2401.10561","repositories_listed":0,"syntology":null},{"url":null,"slug":"phogad-graph-based-anomaly-behavior-detection","title":"PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization","date":"2024-01-19","arxiv_id":"2401.10547","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-optimization-and-machine-learning","title":"Intelligent Optimization and Machine Learning Algorithms for Structural Anomaly Detection using Seismic Signals","date":"2024-01-18","arxiv_id":"2401.10355","repositories_listed":0,"syntology":null},{"url":null,"slug":"melody-robust-semi-supervised-hybrid-model","title":"MELODY: Robust Semi-Supervised Hybrid Model for Entity-Level Online Anomaly Detection with Multivariate Time Series","date":"2024-01-18","arxiv_id":"2401.10338","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gan-based-data-poisoning-framework-against","title":"A GAN-based data poisoning framework against anomaly detection in vertical federated learning","date":"2024-01-17","arxiv_id":"2401.08984","repositories_listed":0,"syntology":null},{"url":null,"slug":"advent-attack-anomaly-detection-in-vanets","title":"ADVENT: Attack/Anomaly Detection in VANETs","date":"2024-01-16","arxiv_id":"2401.08564","repositories_listed":0,"syntology":null},{"url":null,"slug":"pupae-intuitive-and-actionable-explanations","title":"PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies","date":"2024-01-16","arxiv_id":"2401.09489","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-anomaly-detection-for-particle-physics","title":"Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning","date":"2024-01-16","arxiv_id":"2401.08777","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-weird-and-the-wonderful-in-our-solar","title":"The weird and the wonderful in our Solar System: Searching for serendipity in the Legacy Survey of Space and Time","date":"2024-01-16","arxiv_id":"2401.08763","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlad-a-unified-model-for-multi-system-log","title":"MLAD: A Unified Model for Multi-system Log Anomaly Detection","date":"2024-01-15","arxiv_id":"2401.07655","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-modules-improve-modern-image-level","title":"Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study","date":"2024-01-13","arxiv_id":"2401.08686","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-enabled-anomaly-detection-and","title":"Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs","date":"2024-01-13","arxiv_id":"2401.07022","repositories_listed":0,"syntology":null},{"url":null,"slug":"eegformer-towards-transferable-and","title":"EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model","date":"2024-01-11","arxiv_id":"2401.10278","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-spatiotemporal-process-for-multivariate","title":"Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values","date":"2024-01-11","arxiv_id":"2401.05800","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-anomaly-detection-and-explanation-via","title":"Video Anomaly Detection and Explanation via Large Language Models","date":"2024-01-11","arxiv_id":"2401.05702","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-light-weight-and-unsupervised-method-for","title":"A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement","date":"2024-01-10","arxiv_id":"2402.05114","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-analysis-of-anomaly-detection-on","title":"Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods","date":"2024-01-09","arxiv_id":"2401.04437","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-agnostic-face-image-synthesis-detection","title":"Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs","date":"2024-01-08","arxiv_id":"2401.04241","repositories_listed":0,"syntology":null},{"url":null,"slug":"fm-ae-frequency-masked-multimodal-autoencoder","title":"FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis Plate Contact Abnormality Detection","date":"2024-01-08","arxiv_id":"2401.03806","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-anomalies-in-blockchain","title":"Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis","date":"2024-01-07","arxiv_id":"2401.03530","repositories_listed":0,"syntology":null}],"record_sha256":"5f71dad4947875238fd911c7c1caf91f6d3ecbedf2b3ba9b5146d1015bc71c8f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}