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 1–50 of 65 papers

TitleStatusHype
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
Spacing Loss for Discovering Novel CategoriesCode2
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality PropagationCode2
Towards Realistic Semi-Supervised LearningCode1
Neighborhood Contrastive Learning for Novel Class DiscoveryCode1
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class DiscoveryCode1
Open-world Semi-supervised Novel Class DiscoveryCode1
Novel Class Discovery for 3D Point Cloud Semantic SegmentationCode1
On-the-Fly Category DiscoveryCode1
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised LearningCode1
Novel Class Discovery for Ultra-Fine-Grained Visual CategorizationCode1
Novel Class Discovery in Semantic SegmentationCode1
Class-incremental Novel Class DiscoveryCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
Text3DAug -- Prompted Instance Augmentation for LiDAR PerceptionCode1
Parametric Classification for Generalized Category Discovery: A Baseline StudyCode1
Divide and Conquer: Compositional Experts for Generalized Novel Class DiscoveryCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal ImagesCode1
Enhancing Novel Object Detection via Cooperative Foundational ModelsCode1
A Unified Objective for Novel Class DiscoveryCode1
AutoNovel: Automatically Discovering and Learning Novel Visual CategoriesCode1
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral AnalysisCode1
PANDAS: Prototype-based Novel Class Discovery and DetectionCode1
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class DiscoveryCode1
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