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Class Incremental Learning

Papers

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Showing 276–300 of 634 papers

TitleStatusHype
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection—0
AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion—0
Hard ASH: Sparsity and the right optimizer make a continual learner—0
Hebbian Continual Representation Learning—0
Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation—0
Cross-Class Feature Augmentation for Class Incremental Learning—0
Adaptive Discovering and Merging for Incremental Novel Class Discovery—0
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning—0
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning—0
GRASP: A Rehearsal Policy for Efficient Online Continual Learning—0
Memory Is Not the Bottleneck: Cost-Efficient Continual Learning via Weight Space Consolidation—0
Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning—0
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting—0
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning—0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
Controllable Forgetting Mechanism for Few-Shot Class-Incremental Learning—0
Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning—0
CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning—0
Continuous Learning in Single-Incremental-Task Scenarios—0
CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning—0
Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework—0
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection—0
Continual learning with task specialist—0
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring—0
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task—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