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Class Incremental Learning

Papers

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Showing 126–150 of 634 papers

TitleStatusHype
Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental LearningCode1
Continual learning with task specialist—0
Dynamic Integration of Task-Specific Adapters for Class Incremental Learning—0
A preliminary study on continual learning in computer vision using Kolmogorov-Arnold NetworksCode1
ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning—0
Few-Shot Class-Incremental Learning with Non-IID Decentralized Data—0
Knowledge Adaptation Network for Few-Shot Class-Incremental Learning—0
Reducing Catastrophic Forgetting in Online Class Incremental Learning Using Self-Distillation—0
From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion—0
Adaptive Adapter Routing for Long-Tailed Class-Incremental LearningCode0
Joint Input and Output Coordination for Class-Incremental Learning—0
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning—0
Rebalancing Multi-Label Class-Incremental Learning—0
Data-Free Class Incremental Gesture Recognition via Synthetic Feature Sampling—0
AIR: Analytic Imbalance Rectifier for Continual LearningCode1
Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning—0
An Efficient Replay for Class-Incremental Learning with Pre-trained Models—0
Towards Effective Authorship Attribution: Integrating Class-Incremental LearningCode0
AggSS: An Aggregated Self-Supervised Approach for Class-Incremental Learning—0
Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond—0
Balanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentation—0
Multi-Site Class-Incremental Learning with Weighted Experts in EchocardiographyCode0
PIP: Prototypes-Injected Prompt for Federated Class Incremental LearningCode0
FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph LearningCode0
Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation—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