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 41–50 of 65 papers

TitleStatusHype
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
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
Federated Continual Novel Class Learning—0
Novel class discovery meets foundation models for 3D semantic segmentation—0
A Practical Approach to Novel Class Discovery in Tabular DataCode0
Show:102550
← PrevPage 5 of 7Next →
All datasetsCIFAR10cifar100SVHN

Benchmark Results

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