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Class Incremental Learning

Papers

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Showing 351–400 of 634 papers

TitleStatusHype
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey—0
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition—0
Few-shot Class-incremental Learning for Cross-domain Disease Classification—0
Few-Shot Class-Incremental Learning for Named Entity Recognition—0
Few Shot Class Incremental Learning using Vision-Language models—0
MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning—0
Few-Shot Class-Incremental Learning with Non-IID Decentralized Data—0
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning—0
FM-LoRA: Factorized Low-Rank Meta-Prompting for Continual Learning—0
From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
Future-Proofing Class Incremental Learning—0
General Incremental Learning with Domain-aware Categorical Representations—0
Generalizable Two-Branch Framework for Image Class-Incremental Learning—0
Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective—0
Joint Input and Output Coordination for Class-Incremental Learning—0
Just a Glimpse: Rethinking Temporal Information for Video Continual Learning—0
Knowledge Adaptation Network for Few-Shot Class-Incremental Learning—0
Knowledge Consolidation based Class Incremental Online Learning with Limited Data—0
Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning—0
Knowledge Memorization and Rumination for Pre-trained Model-based Class-Incremental Learning—0
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning—0
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning—0
Latent-Insensitive autoencoders for Anomaly Detection—0
Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning—0
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning—0
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning—0
Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental Learning—0
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning—0
Learning Conditional Space-Time Prompt Distributions for Video Class-Incremental Learning—0
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Continual Learning with Evolving Class Ontologies—0
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains—0
Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion—0
Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks—0
Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillation—0
Listen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing—0
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning—0
Long-Tail Class Incremental Learning via Independent Sub-prototype Construction—0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
Make Domain Shift a Catastrophic Forgetting Alleviator in Class-Incremental Learning—0
MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning—0
Generative Feature-driven Image Replay for Continual Learning—0
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning—0
MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL—0
Memorizing Complementation Network for Few-Shot Class-Incremental Learning—0
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning—0
Memory Efficient Class-Incremental Learning for Image Classification—0
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning—0
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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