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Novelty Detection

Scientific Novelty Detection

Papers

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Showing 101–150 of 249 papers

TitleStatusHype
Zero Day Threat Detection Using Graph and Flow Based Security Telemetry—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels—0
TransductGAN: a Transductive Adversarial Model for Novelty Detection—0
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies—0
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach—0
Novelty Detection: A Perspective from Natural Language Processing—0
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control—0
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Out-of-Distribution Detection Without Class Labels—0
In-flight Novelty Detection with Convolutional Neural Networks—0
Improving Novelty Detection using the Reconstructions of Nearest NeighboursCode0
Semantic Novelty Detection in Natural Language DescriptionsCode0
New Perspective on Progressive GANs Distillation for One-class Novelty Detection—0
Adversarially Robust One-class Novelty DetectionCode0
Autoencoder-based Semantic Novelty Detection: Towards Dependable AI-based Systems—0
A Survey on Open Set Recognition—0
Margin-Aware Intra-Class Novelty Identification for Medical ImagesCode0
Evaluating the Use of Reconstruction Error for Novelty Localization—0
Hack The Box: Fooling Deep Learning Abstraction-Based Monitors—0
Continual Novelty DetectionCode0
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection—0
Novelty Detection via Contrastive Learning with Negative Data Augmentation—0
The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detectionCode0
Latent Space Exploration Using Generative Kernel PCA—0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin MachinesCode0
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet AutoencoderCode0
Measuring Novelty in Autonomous Vehicles Motion Using Local Outlier Factor Algorithm—0
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things—0
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector—0
Unsupervised Class-Incremental Learning Through Confusion—0
OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty DetectionCode0
Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis—0
Novelty Detection in Sequential Data by Informed Clustering and ModelingCode0
System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring—0
OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space—0
Novelty Detection with Rotated Contrastive Predictive Coding—0
Constraints on Hebbian and STDP learned weights of a spiking neuron—0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
A Review of Open-World Learning and Steps Toward Open-World Learning Without LabelsCode0
Variational Beam Search for Novelty Detection—0
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text ClassifierCode0
Adaptive Matching of Kernel Means—0
Robot in the mirror: toward an embodied computational model of mirror self-recognition—0
Efficient SVDD Sampling with Approximation Guarantees for the Decision BoundaryCode0
A Nonparametric Unsupervised Learning Approach for Structural Damage Detection—0
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