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Class Incremental Learning

Papers

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Showing 326–350 of 634 papers

TitleStatusHype
Feature Expansion and enhanced Compression for Class Incremental LearningCode0
Few-Shot Class Incremental Learning via Robust Transformer ApproachCode0
Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning—0
Continual Learning in the Presence of Repetition—0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
Few Shot Class Incremental Learning using Vision-Language models—0
Hard ASH: Sparsity and the right optimizer make a continual learner—0
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical ImagesCode0
Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learning—0
Realistic Continual Learning Approach using Pre-trained Models—0
Toward industrial use of continual learning : new metrics proposal for class incremental learning—0
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark—0
Future-Proofing Class Incremental Learning—0
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning—0
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach—0
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning—0
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning—0
Recommendation of data-free class-incremental learning algorithms by simulating future data—0
Enhancing Visual Continual Learning with Language-Guided Supervision—0
Exemplar-Free Class Incremental Learning via Incremental Representation—0
Defying Imbalanced Forgetting in Class Incremental Learning—0
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature RichnessCode0
A Bag of Tricks for Few-Shot Class-Incremental Learning—0
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning—0
Confidence Self-Calibration for Multi-Label Class-Incremental Learning—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