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
Meta Discovery: Learning to Discover Novel Classes given Very Limited DataCode1
Learning to Discover and Detect ObjectsCode1
MetaGCD: Learning to Continually Learn in Generalized Category DiscoveryCode1
Modeling Inter-Class and Intra-Class Constraints in Novel Class DiscoveryCode1
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
NeurNCD: Novel Class Discovery via Implicit Neural Representation—0
Novel Class Discovery for Open Set Raga Classification—0
Novel class discovery meets foundation models for 3D semantic segmentation—0
Novel Class Discovery without Forgetting—0
Open Set Domain Adaptation By Novel Class Discovery—0
Open-Set Representation Learning through Combinatorial Embedding—0
Open-world Machine Learning: A Review and New Outlooks—0
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling—0
Self-Cooperation Knowledge Distillation 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