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Novelty Detection

Scientific Novelty Detection

Papers

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Showing 176–200 of 249 papers

TitleStatusHype
Novelty Detection Via Blurring—0
Outside the Box: Abstraction-Based Monitoring of Neural NetworksCode0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Deep Variational Semi-Supervised Novelty Detection—0
Novelty Detection and Learning from Extremely Weak Supervision—0
Evaluating Research Novelty Detection: Counterfactual Approaches—0
How to detect novelty in textual data streams? A comparative study of existing methods—0
GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection—0
Visualizing Image Content to Explain Novel Image Discovery—0
Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification—0
Normalizing flows for novelty detection in industrial time series data—0
Novelty Detection via Network Saliency in Visual-based Deep Learning—0
Deep Unknown Intent Detection with Margin LossCode0
Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection—0
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles—0
Multi-class Novelty Detection Using Mix-up Technique—0
Reducing Overconfident Errors outside the Known Distribution—0
Psychophysical vs. learnt texture representations in novelty detection—0
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models—0
OCKELM+: Kernel Extreme Learning Machine based One-class Classification using Privileged Information (or KOC+: Kernel Ridge Regression or Least Square SVM with zero bias based One-class Classification using Privileged Information)—0
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series—0
Unsupervised Progressive Learning and the STAM ArchitectureCode0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
Deep Transfer Learning for Multiple Class Novelty DetectionCode0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
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