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Novelty Detection

Scientific Novelty Detection

Papers

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Showing 26–50 of 249 papers

TitleStatusHype
Hypothesis-Driven Deep Learning for Out of Distribution Detection—0
Novelty Detection on Radio Astronomy Data using SignaturesCode0
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
Wasserstein Distance-based Expansion of Low-Density Latent Regions for Unknown Class DetectionCode0
Managing the unknown: a survey on Open Set Recognition and tangential areas—0
Navigating Open Set Scenarios for Skeleton-based Action RecognitionCode1
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models—0
Incremental Object-Based Novelty Detection with Feedback Loop—0
Transductive conformal inference with adaptive scoresCode0
Novelty Detection in Reinforcement Learning with World Models—0
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer—0
OpenPatch: a 3D patchwork for Out-Of-Distribution detection—0
Robust Novelty Detection through Style-Conscious Feature RankingCode0
Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication TextCode1
Environment-biased Feature Ranking for Novelty Detection Robustness—0
Language Models for Novelty Detection in System Call Traces—0
Continual Improvement of Threshold-Based Novelty Detection—0
Information Processing by Neuron Populations in the Central Nervous System: Mathematical Structure of Data and Operations—0
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection—0
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection—0
Adaptive learning of density ratios in RKHS—0
Unsupervised Anomaly Detection via Nonlinear Manifold Learning—0
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly DetectionCode2
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
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