SOTAVerified

Class Incremental Learning

Papers

Showing 301–310 of 634 papers

TitleStatusHype
Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse—0
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach—0
Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning—0
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks—0
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning—0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models—0
Active Class Incremental Learning for Imbalanced Datasets—0
Energy Aligning for Biased Models—0
Endpoints Weight Fusion for Class Incremental Semantic Segmentation—0
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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