SOTAVerified

Class Incremental Learning

Papers

Showing 5175 of 634 papers

TitleStatusHype
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental LearningCode1
A preliminary study on continual learning in computer vision using Kolmogorov-Arnold NetworksCode1
Federated Class-Incremental LearningCode1
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental LearningCode1
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial AttacksCode1
CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental LearningCode1
Few-Shot Class-Incremental Learning by Sampling Multi-Phase TasksCode1
Few-shot Class-incremental Learning for 3D Point Cloud ObjectsCode1
A Theoretical Study on Solving Continual LearningCode1
Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision TransformersCode1
Audio-Visual Class-Incremental LearningCode1
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive PromptCode1
Class-Incremental Learning with Cross-Space Clustering and Controlled TransferCode1
Class-Incremental Learning via Dual AugmentationCode1
Class-Incremental Learning for Wireless Device Identification in IoTCode1
Class Incremental Learning via Likelihood Ratio Based Task PredictionCode1
Class-Incremental Learning with Generative ClassifiersCode1
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature ConsolidationCode1
AIR: Analytic Imbalance Rectifier for Continual LearningCode1
Avalanche: an End-to-End Library for Continual LearningCode1
Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental LearningCode1
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental LearningCode1
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary LearningCode1
Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-TrainingCode1
Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old LabelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1S&B10-stage average accuracy68.18Unverified
2SCR10-stage average accuracy65.98Unverified
3iCaRL10-stage average accuracy63.24Unverified
4LUCIR10-stage average accuracy56.53Unverified
5ABD10-stage average accuracy54.44Unverified
6EWC10-stage average accuracy50.53Unverified
7EMR10-stage average accuracy48.66Unverified
8A-GEM10-stage average accuracy45.76Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07Unverified
2PPCA-CLIPFinal Accuracy69.71Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07Unverified
2PPCA-CLIPFinal Accuracy69.71Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy61.7Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy56.2Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy42.6Unverified