SOTAVerified

Class Incremental Learning

Papers

Showing 351360 of 634 papers

TitleStatusHype
DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning0
Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning0
Dual Embodied-Symbolic Concept Representations for Deep Learning0
Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning0
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay0
Dynamic Feature Learning and Matching for Class-Incremental Learning0
Dynamic Integration of Task-Specific Adapters for Class Incremental Learning0
Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning0
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning0
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Benchmark Results

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