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Incremental Learning

Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.

Papers

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Showing 1–10 of 1371 papers

TitleStatusHype
The Bayesian Approach to Continual Learning: An Overview—0
Balancing the Past and Present: A Coordinated Replay Framework for Federated Class-Incremental Learning—0
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative ExpertsCode0
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic—0
Class-Incremental Learning for Honey Botanical Origin Classification with Hyperspectral Images: A Study with Continual Backpropagation—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental LearningCode0
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental LearningCode1
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection—0
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You NeedCode0
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All datasetsCIFAR-100 - 50 classes + 5 steps of 10 classesCIFAR-100 - 50 classes + 10 steps of 5 classesImageNet100 - 10 stepsCIFAR-100-B0(5steps of 20 classes)ImageNet - 10 stepsCIFAR100-B0(10steps of 10 classes)CIFAR-100 - 50 classes + 25 steps of 2 classesCIFAR100B020Step(5ClassesPerStep)ImageNet-100 - 50 classes + 10 steps of 5 classesImageNet-100 - 50 classes + 5 steps of 10 classesCIFAR-100 - 50 classes + 2 steps of 25 classesImageNet - 500 classes + 10 steps of 50 classes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RMM (ResNet-18)Average Incremental Accuracy78.47—Unverified
2DERAverage Incremental Accuracy77.73—Unverified
3FOSTERAverage Incremental Accuracy77.54—Unverified
4CCIL-SDAverage Incremental Accuracy76.77—Unverified
5PODNetAverage Incremental Accuracy73.14—Unverified