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 31–40 of 65 papers

TitleStatusHype
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
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All datasetsCIFAR10cifar100SVHN

Benchmark Results

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