A review of novelty detection

UCI Machine Learning Repository

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Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

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Novelty detection approach for the monitoring of structural vibrations using vision-based mean frequency maps

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Weibull-Open-World (WOW) Multi-Type Novelty Detection in CartPole3D

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Encouraging Curiosity in Case-Based Reasoning and Recommender Systems

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A Typology of Data Anomalies

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Automatic Hand Sign Recognition: Identify Unusuality Through Latent Cognizance

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Subspace clustering for situation assessment in aquatic drones

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Gas Turbine Engine Condition Monitoring Using Gaussian Mixture and Hidden Markov Models

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How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise

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Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition

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Multi-class Novelty Detection Using Mix-up Technique

Bhattacharjee, S., Mandal, D., & Biswas, S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093303 · Cited by 10

Anomaly Detection Forest

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Hardware Fingerprinting for the ARINC 429 Avionic Bus

Markevich, N. G., & Wool, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59013-0_3 · Cited by 10

Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems

Rausch, A., Sedeh, A. M., & Zhang, M. (2021) · Applied Sciences · doi:10.3390/app11219881 · Cited by 10

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Nentwich, C., & Reinhart, G. (2021) · Applied Sciences · doi:10.3390/app112110403 · Cited by 10

Keeping Deep Lithography Simulators Updated: Global–Local Shape-Based Novelty Detection and Active Learning

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Dimensionality Reduction Methods of a Clustered Dataset for the Diagnosis of a SCADA-Equipped Complex Machine

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A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection

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Clustering by Intent: A Semi-Supervised Method to Discover Relevant Clusters Incrementally

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Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain

Carino, J. A., Saucedo-Dorantes, J. J., Zurita, D., Delgado-Prieto, M., Ortega, J. A., Osornio-Rios, R. A., & Romero-Troncoso, R. D. J. (2016) · Shock and Vibration · doi:10.1155/2016/2417856 · Cited by 10

Automatic POI Matching Using an Outlier Detection Based Approach

Almeida, A., Alves, A., & Gomes, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01768-2_4 · Cited by 10

Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

Contreras-Cruz, M. A., Ramirez‐Paredes, J., Hernandez-Belmonte, U. H., & Ayala-Ramírez, V. (2019) · Sensors · doi:10.3390/s19132965 · Cited by 12

Machine learning-assisted virtual patching of web applicationsPreprint

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Deep Variational Semi-Supervised Novelty DetectionPreprint

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On the Nature and Types of Anomalies: A Review.Preprint

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Anomaly Detection using Generative Adversarial Networks Reviewing methodological progress and challenges

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Advanced Defect Detection in Wrap Film Products: A Hybrid Approach with Convolutional Neural Networks and One-Class Support Vector Machines with Variational Autoencoder-Derived Covariance Vectors

Shimizu, T., Nagata, F., Habib, M. K., Arima, K., Otsuka, A., & Watanabe, K. (2024) · Machines · doi:10.3390/machines12090603 · Cited by 9

Non-Intrusive Low-Cost IoT-Based Hardware System for Sustainable Predictive Maintenance of Industrial Pump Systems

Brito, S., Azinheira, G., Semião, J., Sousa, N., & Litrán, S. P. (2025) · Electronics · doi:10.3390/electronics14142913 · Cited by 9

Damage Detection on Real Bridges Using Machine Learning Techniques: A Systematic Review

Santos-Vila, I., Soto, R., Vega, E., Crawford, B., & Peña, Á. (2025) · Applied Sciences · doi:10.3390/app15168884 · Cited by 10

Novelty Detection in Social Media by Fusing Text and Image Into a Single Structure

de Amorim, M. T. C. F., Bortoloti, F. D., Ciarelli, P. M., Salles, E. O. T., & Cavalieri, D. C. (2019) · IEEE Access · doi:10.1109/access.2019.2939736 · Cited by 9

Combining Boundary Detector and SND-SVM for Fast Learning

Yi, Y., Shi, Y., Wang, W., Lei, G., Dai, J., & Zheng, H. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01196-2 · Cited by 9

Machine Learning for Diagnosis of Event Synchronization Faults in Discrete Manufacturing Systems

Cohen, J., Jiang, B., & Ni, J. (2021) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4052762 · Cited by 9

EveSyncIAI: Event Synchronization Industrial Augmented Intelligence for Fault Diagnosis

Cohen, J., Jiang, B., & Ni, J. (2022) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2022.3179669 · Cited by 8

Incremental Learning of Human Activities in Smart Homes

Chua, S.-L., Foo, L. K., Guesgen, H. W., & Marsland, S. (2022) · Sensors · doi:10.3390/s22218458 · Cited by 9

Non-Pattern-Based Anomaly Detection in Time-Series

Tkach, V., Кудін, А. М., Kebande, V. R., Baranovskyi, O., & Kudin, I. A. (2023) · Electronics · doi:10.3390/electronics12030721 · Cited by 10

Corner cases in machine learning processes

Heidecker, F., Bieshaar, M., & Sick, B. (2024) · AI Perspectives & Advances · doi:10.1186/s42467-023-00015-y · Cited by 10

Embedded event detection for self-aware and safer assets

Katsouros, V., Koulamas, C., Fournaris, A. P., & Emmanouilidis, C. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.09.625 · Cited by 9

Misuse of ‘Break-the-Glass' Policies in Hospitals

Stark, B., Gewald, H., Lautenbacher, H., Haase, U., & Ruff, S. (2018) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.2018070106 · Cited by 9

Fuzzy One-Class Extreme Auto-encoder

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A hybrid heuristic algorithm for evolving models in simultaneous scenarios of classification and clustering

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A Robust and Sparse Process Fault Detection Method Based on RSPCA

Peng, P., Zhang, Y., Liu, F., Wang, H., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2928329 · Cited by 9

Data-driven Detection of Multimessenger Transients

Sadeh, I. (2020) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ab8b5f · Cited by 10

Novelty Detection Review State of Art and Discussion of New Innovations in The Main Application Domains

Ouafae, B., Oumaima, L., Mariam, R., & Lyhyaoui, A. (2020) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset48871.2020.9092147 · Cited by 9

Shallow aquifer monitoring using handpump vibration data

Manandhar, A., Greeff, H., Thomson, P., Hope, R., & Clifton, D. A. (2020) · Journal of Hydrology X · doi:10.1016/j.hydroa.2020.100057 · Cited by 8

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism

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Learning with an augmented (unknown) class using neural networks

Engelbrecht, E. R., & du Preez, J. A. (2020) · Scientific African · doi:10.1016/j.sciaf.2020.e00600 · Cited by 9

A Comparative Study of Deep Learning Architectures for Detection of Anomalous ADS-B Messages

Karam, R., Salomon, M., & Couturier, R. (2020) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit49905.2020.9263880 · Cited by 12

A computational model for subjective evaluation of novelty in descriptive aptitude

Chaudhuri, N. B., Dhar, D., & Yammiyavar, P. G. (2020) · International Journal of Technology and Design Education · doi:10.1007/s10798-020-09638-2 · Cited by 12

Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Górski, J., Jabłoński, A., Heesch, M., Dziendzikowski, M., & Dworakowski, Z. (2021) · Sensors · doi:10.3390/s21103536 · Cited by 9

Machine-learning based feature selection for a non-invasive breathing change detection

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Automating assessment of design exams: A case study of novelty evaluation

Chaudhuri, N. B., Dhar, D., & Yammiyavar, P. G. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116108 · Cited by 10

Big Data Analysis for Media Production

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Video feature descriptor combining motion and appearance cues with length-invariant characteristics

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Self-running Fault Diagnosis Method for Rolling Element Bearing

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Novelty Detection in Passive Sonar Systems using Stacked AutoEncoders

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Physical Layer Anomaly Detection Mechanisms in IoT Networks

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A deep learning-based conditional system health index method to reduce the uncertainty of remaining useful life prediction

Jang, J. (2022) · Soft Computing · doi:10.1007/s00500-022-07625-4 · Cited by 8

EnsMOD: A Software Program for Omics Sample Outlier Detection

Manes, N. P., Song, J. T., & Nita‐Lazar, A. (2023) · Journal of Computational Biology · doi:10.1089/cmb.2022.0243 · Cited by 8

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

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A convolution neural network-based technique for health monitoring of connections of a multi-story 3D steel frame structure

Naresh, M., Kumar, V., & Pal, J. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00424-4 · Cited by 8

Novelty detection for long-term diagnostic data with Gaussian and non-Gaussian disturbances using a support vector machine

Moosavi, F., Shiri, H., Vashishtha, G., Chauhan, S., Wyłomańska, A., & Zimroz, R. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad90fe · Cited by 8

A new health indicator for rotating machinery condition monitoring under variable operation conditions through regression among vibration features

Rao, M., Yang, X., Chen, Y., Zuo, M. J., Bu, Z., Jin, Y., & Chu, F. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113447 · Cited by 8

Accurate Defect Detection in Thin-Wall Structures With Transducer Networks via Outlier Elimination

Li, X., Ding, S., & Tan, B. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2871459 · Cited by 8

Reference-free method for investigating classification uncertainty in large-scale land cover datasets

Chen, P., Huang, H., & Shi, W. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102673 · Cited by 8

Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques

Cheng, C., Chen, P.-W., & Wu, Y.-T. (2023) · Applied Sciences · doi:10.3390/app13042147 · Cited by 8

Autoencoder and Incremental Clustering-Enabled Anomaly Detection

Connelly, A. C., Zaidi, S. A. R., & McLernon, D. (2023) · Electronics · doi:10.3390/electronics12091970 · Cited by 9

Bidirectional piecewise linear representation of time series with application to collective anomaly detection

Shi, W. H., Azzopardi, G., Karastoyanova, D., & Huang, Y. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102155 · Cited by 8

Understanding the effects of natural hazards on chemical emission incidents using machine learning techniques

Yang, H., Li, C., Zou, L., & Wang, Q. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.106900 · Cited by 8

Large-Scale Classification by an Approximate Least Squares One-Class Support Vector Machine Ensemble

Mygdalis, V., Alexandros, I., Tefas, A., & Pitas, I. (2015) · doi:10.1109/trustcom-bigdatase-ispa.2015.555 · Cited by 8

A Data-Driven Approach For Actuator Servo Loop Failure Detection

Urbano, S., Chaumette, É., Goupil, P., & Tourneret, J. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.2353 · Cited by 8

Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration

Reyes-Garcia, J., Galeana‐Zapién, H., Galaviz‐Mosqueda, A., & Torres-Huitzil, C. (2018) · IEEE Access · doi:10.1109/access.2018.2853701 · Cited by 8

An Adaptive Sliding-Window Strategy for Outlier Detection in Wireless Sensor Networks for Smart Port Construction

Yao, H., Yang, Y., Fu, X., & Mi, C. (2018) · Journal of Coastal Research · doi:10.2112/si82-036.1 · Cited by 8

Determining user needs through abnormality detection and heterogeneous embedding of usage sequence

Lee, Y., Cho, S., & Choi, J. (2019) · Electronic Commerce Research · doi:10.1007/s10660-019-09347-6 · Cited by 7

Anomaly Detection in Smart Homes Using Deep Learning

Moallem, M., Hassanpour, H., & Pouyan, A. A. (2019) · Iranica Journal of Energy and Environment · doi:10.5829/ijee.2019.10.02.10 · Cited by 8

Robust hierarchical clustering for novelty identification in sensor networks: With applications to industrial systems

Maleki, S., & Bingham, C. M. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105771 · Cited by 8

The robustness-fidelity trade-off in Grow When Required neural networks performing continuous novelty detection

Pitonakova, L., & Bullock, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.10.015 · Cited by 8

Two-Dimensional Aortic Size Normalcy: A Novelty Detection Approach

Frasconi, P., Baracchi, D., Giusti, B., Kura, A., Spaziani, G., Cherubini, A., Favilli, S., Lenarda, A. D., Pepe, G., & Nistri, S. (2021) · Diagnostics · doi:10.3390/diagnostics11020220 · Cited by 8

Generalized One-Class Learning Using Pairs of Complementary Classifiers

Cherian, A., & Wang, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3092999 · Cited by 8

MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach

Gashi, M., Gursch, H., Hinterbichler, H., Pichler, S., Lindstaedt, S., & Thalmann, S. (2022) · Sensors · doi:10.3390/s22082837 · Cited by 8

On the Construction of Extreme Learning Machine for One Class Classifier

Gautam, C., & Tiwari, A. (2015) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-319-28397-5_35 · Cited by 8

Online Outlier Detection of Energy Data Streams Using Incremental and Kernel PCA Algorithms

Deng, J. D. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0062 · Cited by 8

Ensemble-Bayesian SPC: Multi-mode process monitoring for novelty detection

Bacher, M., & Ben‐Gal, I. (2017) · IISE Transactions · doi:10.1080/24725854.2017.1347984 · Cited by 8

None of the above: A Bayesian account of the detection of novel categories.

Navarro, D., & Kemp, C. (2017) · Psychological Review · doi:10.1037/rev0000077 · Cited by 8

One-Class Feature Learning Using Intra-Class Splitting

Schlachter, P., Liao, Y.-W., & Yang, B. (2019) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8902848 · Cited by 8

Time series based behavior pattern quantification analysis and prediction — A study on animal behavior

Jiang, W., Wang, K., Lv, Y. A., Guo, J., Ni, Z., & Ni, Y. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122884 · Cited by 8

Sequential Model-Free Anomaly Detection for Big Data Streams

Kurt, M. N., Yılmaz, Y., & Wang, X. (2019) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2019.8919759 · Cited by 8

Abnormal SDN switches detection based on chaotic analysis of network traffic

Dinh, P. T., Lee, T., Canh, T. N., Dang, S. P., Noh, S. K., & Park, M. (2019) · Asia-Pacific Conference on Communications (APCC) · doi:10.1109/apcc47188.2019.9026485 · Cited by 8

Policy Entropy for Out-of-Distribution Classification

Sedlmeier, A., Müller, R., Illium, S., & Linnhoff–Popien, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61616-8_34 · Cited by 8

One-Class Ensembles for Rare Genomic Sequences Identification

Kaufmann, J., Asalone, K. C., Corizzo, R., Saldanha, C. J., Bracht, J. R., & Japkowicz, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61527-7_23 · Cited by 8

Data-driven Detection of Multimessenger Transients

Sadeh, I. (2020) · Desy Publications Database (Deutsches Elektronen-Synchrotron DESY) · Cited by 8

Rare bioparticle detection via deep metric learning

Luo, S., Shi, Y., Chin, L. K., Zhang, Y., Wen, B., Sun, Y., Nguyễn, B. T. T., Chierchia, G., Talbot, H., Bourouina, T., et al. (2021) · RSC Advances · doi:10.1039/d1ra02869c · Cited by 8

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

Vibhorpandhare, Jia, X., & Lee, J. (2021) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2021.v13i1.2989 · Cited by 8

Anomaly detection in the probability simplex under different geometries

Legaria-Peña, J. U., Mota, S., Martinez, S., Cobá, A., Chable, A., & Neme, A. (2023) · Information Geometry · doi:10.1007/s41884-023-00107-y · Cited by 7

Exploring AI-based Computational Models of Novelty to Encourage Curiosity in Student Learning

Mohseni, M., Maher, M. L., Grace, K. S., Siddiqui, S., & Najjar, N. A. (2024) · SN Computer Science · doi:10.1007/s42979-024-02837-x · Cited by 7

Applicability domain of a calibration model based on neural networks and infrared spectroscopy

Rodríguez-Barrios, M. S., Ferré, J., Larrechi, M. S., & Ruiz, E. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105242 · Cited by 7

Robust outlier detection method based on local entropy and global density

Zhang, K., Zhang, B., Huang, W. M., Gao, H., Xu, N., & Wan, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128424 · Cited by 7

Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables

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Algorithm based on modified angle‐based outlier factor for open‐set classification of text documents

Walkowiak, T., Datko, S., & Maciejewski, H. (2018) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2388 · Cited by 7

www.elsevier.com/locate/sigpro Noveltydetection: a review—part 1: statistical approaches

Markou, M., & Singh, S. (2003)

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