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Class Incremental Learning

Papers

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Showing 501–525 of 634 papers

TitleStatusHype
Completely Weakly Supervised Class-Incremental Learning for Semantic Segmentation—0
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection—0
A Novel Progressive Multi-label Classifier for Classincremental Data—0
An experimental approach on Few Shot Class Incremental Learning—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
TKIL: Tangent Kernel Approach for Class Balanced Incremental Learning—0
TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot 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
A New Benchmark for Few-Shot Class-Incremental Learning: Redefining the Upper Bound—0
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing—0
PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning—0
PyCIL: A Python Toolbox for Class-Incremental Learning—0
Topology-Preserving Class-Incremental Learning—0
An Efficient Replay for Class-Incremental Learning with Pre-trained Models—0
Toward industrial use of continual learning : new metrics proposal for class incremental learning—0
Realistic Continual Learning Approach using Pre-trained Models—0
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning—0
Rebalancing Multi-Label Class-Incremental Learning—0
Recent Advances of Foundation Language Models-based Continual Learning: A Survey—0
Recommendation of data-free class-incremental learning algorithms by simulating future data—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