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
AutoNovel: Automatically Discovering and Learning Novel Visual CategoriesCode1
Neighborhood Contrastive Learning for Novel Class DiscoveryCode1
Meta Discovery: Learning to Discover Novel Classes given Very Limited DataCode1
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal ImagesCode1
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
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Hierarchical novel class discovery for single-cell transcriptomic profilesCode0
NC-NCD: Novel Class Discovery for Node ClassificationCode0
Self-Cooperation Knowledge Distillation for Novel Class Discovery—0
Exclusive Style Removal for Cross Domain Novel Class Discovery—0
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
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
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All datasetsCIFAR10cifar100SVHN

Benchmark Results

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