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 1–50 of 65 papers

TitleStatusHype
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality PropagationCode2
Spacing Loss for Discovering Novel CategoriesCode2
Text3DAug -- Prompted Instance Augmentation for LiDAR PerceptionCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
Novel Class Discovery for Ultra-Fine-Grained Visual CategorizationCode1
PANDAS: Prototype-based Novel Class Discovery and DetectionCode1
Enhancing Novel Object Detection via Cooperative Foundational ModelsCode1
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised LearningCode1
MetaGCD: Learning to Continually Learn in Generalized Category DiscoveryCode1
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral AnalysisCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
Open-world Semi-supervised Novel Class DiscoveryCode1
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class DiscoveryCode1
Novel Class Discovery for 3D Point Cloud Semantic SegmentationCode1
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class DiscoveryCode1
On-the-Fly Category DiscoveryCode1
Parametric Classification for Generalized Category Discovery: A Baseline StudyCode1
Learning to Discover and Detect ObjectsCode1
Modeling Inter-Class and Intra-Class Constraints in Novel Class DiscoveryCode1
Class-incremental Novel Class DiscoveryCode1
Towards Realistic Semi-Supervised LearningCode1
Divide and Conquer: Compositional Experts for Generalized Novel Class DiscoveryCode1
Novel Class Discovery in Semantic SegmentationCode1
A Unified Objective for Novel Class DiscoveryCode1
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