SOTAVerified

Novel Class Discovery

The goal of Novel Class Discovery (NCD) is to identify new classes in unlabeled data, by exploiting prior knowledge from known classes. In this specific setup, the data is split in two sets. The first is a labeled set containing known classes and the second is an unlabeled set containing unknown classes that must be discovered.

Papers

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Showing 26–50 of 65 papers

TitleStatusHype
Text3DAug -- Prompted Instance Augmentation for LiDAR PerceptionCode1
Enhancing Novel Object Detection via Cooperative Foundational ModelsCode1
Towards Realistic Semi-Supervised LearningCode1
A Unified Objective for Novel Class DiscoveryCode1
Découvrir de nouvelles classes dans des données tabulairesCode0
An Interactive Interface for Novel Class Discovery in Tabular DataCode0
Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class DiscoveryCode0
NC-NCD: Novel Class Discovery for Node ClassificationCode0
Novel Class Discovery: an Introduction and Key ConceptsCode0
A Practical Approach to Novel Class Discovery in Tabular DataCode0
Supervised Knowledge May Hurt Novel Class Discovery PerformanceCode0
A Method for Discovering Novel Classes in Tabular DataCode0
Hierarchical novel class discovery for single-cell transcriptomic profilesCode0
Novel Class Discovery for Long-tailed RecognitionCode0
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery—0
Adaptive Discovering and Merging for Incremental Novel Class Discovery—0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Continual Novel Class Discovery via Feature Enhancement and Adaptation—0
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution—0
Debiased Novel Category Discovering and Localization—0
Exclusive Style Removal for Cross Domain Novel Class Discovery—0
Federated Continual Novel Class Learning—0
Freeze and Cluster: A Simple Baseline for Rehearsal-Free Continual Category Discovery—0
Mutual Information-guided Knowledge Transfer for Novel Class Discovery—0
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All datasetsCIFAR10cifar100SVHN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoNovelClustering Accuracy0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AutoNovelClustering Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1AutoNovelClustering Accuracy0.95—Unverified