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Novelty Detection

Scientific Novelty Detection

Papers

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Showing 1–50 of 249 papers

TitleStatusHype
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly DetectionCode2
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADCode2
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
Open-Set Gait Recognition from Sparse mmWave Radar Point CloudsCode1
Navigating Open Set Scenarios for Skeleton-based Action RecognitionCode1
Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication TextCode1
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution DetectionCode1
OpenAUC: Towards AUC-Oriented Open-Set RecognitionCode1
Semantic Novelty Detection via Relational ReasoningCode1
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty DetectionCode1
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency RegularizationCode1
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future ChallengesCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with OutliersCode1
CreativeGAN: Editing Generative Adversarial Networks for Creative Design SynthesisCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Latent World Models For Intrinsically Motivated ExplorationCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Stream-51: Streaming Classification and Novelty Detection from VideosCode1
MorphoCluster: Efficient Annotation of Plankton images by ClusteringCode1
RaPP: Novelty Detection with Reconstruction along Projection PathwayCode1
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training ParadigmCode1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty DetectionCode1
One-Class Convolutional Neural NetworkCode1
Harnessing Large Language Models for Scientific Novelty Detection—0
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect—0
AquaSignal: An Integrated Framework for Robust Underwater Acoustic AnalysisCode0
Isolation Forest in Novelty Detection Scenario—0
GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation—0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems—0
Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained Novelty Detection in Medical Imaging—0
A Contrastive Teacher-Student Framework for Novelty Detection under Style Shifts—0
Killing it with Zero-Shot: Adversarially Robust Novelty DetectionCode0
Score Combining for Contrastive OOD Detection—0
Uncertainty Quantification in Continual Open-World Learning—0
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty DetectionCode0
CONCLAD: COntinuous Novel CLAss Detector—0
On Design Choices in Similarity-Preserving Sparse Randomized Embeddings—0
Representational learning for an anomalous sound detection system with source separation model—0
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor—0
NovAScore: A New Automated Metric for Evaluating Document Level Novelty—0
Unsupervised Novelty Detection Methods Benchmarking with Wavelet DecompositionCode0
Damage detection in an uncertain nonlinear beam based on stochastic Volterra series: an experimental application—0
Universal Novelty Detection Through Adaptive Contrastive LearningCode0
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty DetectionCode0
Comparison Visual Instruction Tuning—0
Remembering Transformer for Continual Learning—0
Beyond the Known: Adversarial Autoencoders in Novelty Detection—0
Hypothesis-Driven Deep Learning for Out of Distribution Detection—0
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