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Class Incremental Learning

Papers

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Showing 151–200 of 634 papers

TitleStatusHype
Adaptive Aggregation Networks for Class-Incremental LearningCode1
Continual Prototype Evolution: Learning Online from Non-Stationary Data StreamsCode1
RODEO: Replay for Online Object DetectionCode1
Brain-inspired replay for continual learning with artificial neural networksCode1
GDumb: A Simple Approach that Questions Our Progress in Continual LearningCode1
Insights from the Future for Continual LearningCode1
Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old LabelsCode1
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental LearningCode1
Few-Shot Class-Incremental LearningCode1
Generative Feature Replay For Class-Incremental LearningCode1
Dark Experience for General Continual Learning: a Strong, Simple BaselineCode1
Semantic Drift Compensation for Class-Incremental LearningCode1
Mnemonics Training: Multi-Class Incremental Learning without ForgettingCode1
Maintaining Discrimination and Fairness in Class Incremental LearningCode1
Rehearsal-Free Continual Learning over Small Non-I.I.D. BatchesCode1
On Tiny Episodic Memories in Continual LearningCode1
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial AttacksCode1
Overcoming catastrophic forgetting in neural networksCode1
iCaRL: Incremental Classifier and Representation LearningCode1
The Bayesian Approach to Continual Learning: An Overview—0
Balancing the Past and Present: A Coordinated Replay Framework for Federated Class-Incremental Learning—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
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection—0
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition—0
StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning—0
Listen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting—0
Completely Weakly Supervised Class-Incremental Learning for Semantic Segmentation—0
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection—0
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning—0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
Autoencoder-Based Hybrid Replay for Class-Incremental Learning—0
Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillation—0
Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse—0
Noise-Tolerant Coreset-Based Class Incremental Continual Learning—0
Audio-Visual Class-Incremental Learning for Fish Feeding intensity Assessment in Aquaculture—0
Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning—0
MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL—0
GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning—0
Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning—0
CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental LearningCode0
LoRAX: LoRA eXpandable Networks for Continual Synthetic Image AttributionCode0
FM-LoRA: Factorized Low-Rank Meta-Prompting for Continual Learning—0
GOTHAM: Graph Class Incremental Learning Framework under Weak SupervisionCode0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
Diffusion Meets Few-shot Class Incremental Learning—0
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in 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