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Class Incremental Learning

Papers

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Showing 401–425 of 634 papers

TitleStatusHype
RMM: Reinforced Memory Management for Class-Incremental LearningCode0
Effective Decision Boundary Learning for Class Incremental Learning—0
Online Class-Incremental Learning For Real-World Food Image ClassificationCode0
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning—0
Online Hyperparameter Optimization for Class-Incremental LearningCode1
Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning—0
NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual LearningCode1
Space-time Prompting for Video Class-incremental Learning—0
Data-Free Class-Incremental Hand Gesture RecognitionCode1
Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning—0
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning—0
Towards Realistic Evaluation of Industrial Continual Learning Scenarios with an Emphasis on Energy Consumption and Computational FootprintCode0
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning—0
Few-Shot Class-Incremental Learning via Class-Aware Bilateral DistillationCode1
CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning—0
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task—0
Endpoints Weight Fusion for Class Incremental Semantic Segmentation—0
Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference—0
Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental LearningCode1
Scalable Learning with Incremental Probabilistic PCACode0
Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental LearningCode0
Evidential Deep Learning for Class-Incremental Semantic Segmentation—0
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection—0
Continual Learning with Optimal Transport based Mixture Model—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
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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