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Class Incremental Learning

Papers

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Showing 351–375 of 634 papers

TitleStatusHype
SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual LearningCode0
Just a Glimpse: Rethinking Temporal Information for Video Continual Learning—0
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning—0
Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information ExtractionCode0
Condensed Prototype Replay for Class Incremental Learning—0
Dealing with Cross-Task Class Discrimination in Online Continual LearningCode0
SRIL: Selective Regularization for Class-Incremental Learning—0
Evolving Dictionary Representation for Few-shot Class-incremental Learning—0
Adversary Aware Continual Learning—0
A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill RecognitionCode0
eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation—0
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning—0
A Survey on Few-Shot Class-Incremental Learning—0
Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features—0
Preserving Locality in Vision Transformers for Class Incremental LearningCode0
Class-Incremental Learning of Plant and Disease Detection: Growing Branches with Knowledge Distillation—0
PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark—0
Few-shot Class-incremental Learning for Cross-domain Disease Classification—0
Taxonomic Class Incremental Learning—0
MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning—0
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Cross-Class Feature Augmentation for Class Incremental Learning—0
On the Stability-Plasticity Dilemma of Class-Incremental Learning—0
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental LearningCode1
Semantic-visual Guided Transformer for Few-shot Class-incremental Learning—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