SOTAVerified

Class Incremental Learning

Papers

Showing 125 of 634 papers

TitleStatusHype
MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental LearningCode4
External Knowledge Injection for CLIP-Based Class-Incremental LearningCode2
Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter FusionCode2
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language ModelsCode2
Pre-trained Vision and Language Transformers Are Few-Shot Incremental LearnersCode2
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental LearningCode2
Class-incremental Learning for Time Series: Benchmark and EvaluationCode2
PILOT: A Pre-Trained Model-Based Continual Learning ToolboxCode2
Class-Incremental Learning: A SurveyCode2
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental LearningCode2
Supervised Contrastive LearningCode2
Three scenarios for continual learningCode2
Learning without ForgettingCode2
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental LearningCode1
PrePrompt: Predictive prompting for class incremental learningCode1
Adaptive Decision Boundary for Few-Shot Class-Incremental LearningCode1
Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-TrainingCode1
Reducing Class-wise Confusion for Incremental Learning with Disentangled ManifoldsCode1
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental LearningCode1
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental LearningCode1
Task-Agnostic Guided Feature Expansion for Class-Incremental LearningCode1
Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity GroupingCode1
Navigating Semantic Drift in Task-Agnostic Class-Incremental LearningCode1
Continuous Knowledge-Preserving Decomposition for Few-Shot Continual LearningCode1
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware ClassificationCode1
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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