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Class Incremental Learning

Papers

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Showing 426–450 of 634 papers

TitleStatusHype
Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features—0
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning—0
On the Stability-Plasticity Dilemma of Class-Incremental Learning—0
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning—0
Open-world Machine Learning: A Review and New Outlooks—0
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator—0
Overcoming the Stability Gap in Continual Learning—0
PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning—0
PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning—0
PBES: PCA Based Exemplar Sampling Algorithm for Continual Learning—0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation—0
Plastic Learning with Deep Fourier Features—0
PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning—0
PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark—0
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection—0
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning—0
Probing Image Compression For Class-Incremental Learning—0
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning—0
Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection—0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning—0
Prototypical quadruplet for few-shot class incremental learning—0
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing—0
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All datasetscifar100CIFAR-100 - 50 classes + 10 steps of 5 classesCIFAR-100 - 50 classes + 5 steps of 10 classesCifar100-B0(10 tasks)-no-exemplarsCifar100-B0(20 tasks)-no-exemplarsCIFAR100-B0(50 tasks)-no-exemplars

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1S&B10-stage average accuracy68.18—Unverified
2SCR10-stage average accuracy65.98—Unverified
3iCaRL10-stage average accuracy63.24—Unverified
4LUCIR10-stage average accuracy56.53—Unverified
5ABD10-stage average accuracy54.44—Unverified
6EWC10-stage average accuracy50.53—Unverified
7EMR10-stage average accuracy48.66—Unverified
8A-GEM10-stage average accuracy45.76—Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07—Unverified
2PPCA-CLIPFinal Accuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07—Unverified
2PPCA-CLIPFinal Accuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy61.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy56.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy42.6—Unverified