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Class Incremental Learning

Papers

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Showing 251–275 of 634 papers

TitleStatusHype
Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental LearningCode1
Class incremental learning with probability dampening and cascaded gated classifierCode0
Few-Shot Class-Incremental Learning with Prior KnowledgeCode0
Bias Mitigating Few-Shot Class-Incremental Learning—0
PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental LearningCode0
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning—0
Divide and not forget: Ensemble of selectively trained experts in Continual LearningCode0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning—0
Class-Incremental Learning for Multi-Label Audio Classification—0
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning—0
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental LearningCode1
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental LearningCode1
Class Incremental Learning with Multi-Teacher DistillationCode0
DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental LearningCode0
Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning—0
Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning—0
Generative Multi-modal Models are Good Class Incremental LearnersCode1
FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental LearningCode1
Long-Tail Class Incremental Learning via Independent Sub-prototype Construction—0
NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental LearningCode1
Federated Class-Incremental Learning with New-Class Augmented Self-DistillationCode1
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language ModelsCode1
Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental LearningCode0
Continual Learning: Forget-free Winning Subnetworks for Video RepresentationsCode1
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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