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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 65 papers

TitleStatusHype
NeurNCD: Novel Class Discovery via Implicit Neural Representation—0
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution—0
Freeze and Cluster: A Simple Baseline for Rehearsal-Free Continual Category Discovery—0
Novel Class Discovery for Open Set Raga Classification—0
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Hierarchical novel class discovery for single-cell transcriptomic profilesCode0
Text3DAug -- Prompted Instance Augmentation for LiDAR PerceptionCode1
NC-NCD: Novel Class Discovery for Node ClassificationCode0
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
Self-Cooperation Knowledge Distillation for Novel Class Discovery—0
Exclusive Style Removal for Cross Domain Novel Class Discovery—0
Novel Class Discovery for Ultra-Fine-Grained Visual CategorizationCode1
Continual Novel Class Discovery via Feature Enhancement and Adaptation—0
TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen—0
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation—0
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery—0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality PropagationCode2
Adaptive Discovering and Merging for Incremental Novel Class Discovery—0
Open-world Machine Learning: A Review and New Outlooks—0
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling—0
Debiased Novel Category Discovering and Localization—0
PANDAS: Prototype-based Novel Class Discovery and DetectionCode1
Federated Continual Novel Class Learning—0
Show:102550
← PrevPage 1 of 3Next →
All datasetsCIFAR10cifar100SVHN

Benchmark Results

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