Continual Learning

Continual Learning (also known as Incremental Learning, Life-long Learning) is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available anymore during training new ones.
If not mentioned, the benchmarks here are Task-CL, where task-id is provided on validation.

Source:
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Three scenarios for continual learning
Lifelong Machine Learning
Continual lifelong learning with neural networks: A review

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 51–100 of 2644 papers

Title	Date	Tasks	Status	Hype
Rethinking Continual Learning with Progressive Neural Collapse	May 30, 2025	Continual Learning	—Unverified	0
Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection	May 30, 2025	Audio Deepfake DetectionContinual Learning	CodeCode Available	0
When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways	May 30, 2025	Continual LearningImage Augmentation	CodeCode Available	2
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data	May 30, 2025	Continual LearningFew-Shot Learning	CodeCode Available	1
BIRD: Behavior Induction via Representation-structure Distillation	May 29, 2025	Continual LearningFairness	—Unverified	0
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning	May 29, 2025	Continual Learning	CodeCode Available	1
Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning	May 28, 2025	Continual Learningparameter-efficient fine-tuning	—Unverified	0
SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting	May 28, 2025	Continual Learning	—Unverified	0
Budget-Adaptive Adapter Tuning in Orthogonal Subspaces for Continual Learning in LLMs	May 28, 2025	Continual Learning	—Unverified	0
IRS: Incremental Relationship-guided Segmentation for Digital Pathology	May 28, 2025	Continual LearningDomain Generalization	CodeCode Available	0
MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking	May 28, 2025	Activity RecognitionContinual Learning	—Unverified	0
Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective	May 27, 2025	Continual Learning	—Unverified	0
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning	May 26, 2025	Continual LearningDataset Distillation	—Unverified	0
Beyond Freezing: Sparse Tuning Enhances Plasticity in Continual Learning with Pre-Trained Models	May 26, 2025	Continual Learning	CodeCode Available	0
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning	May 26, 2025	Continual LearningMissing Labels	CodeCode Available	0
MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering	May 26, 2025	Continual LearningQuestion Answering	CodeCode Available	0
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study	May 24, 2025	Continual Learning	CodeCode Available	1
Exemplar-Free Continual Learning for State Space Models	May 24, 2025	Continual LearningExemplar-Free	—Unverified	0
Knowledge Grafting of Large Language Models	May 24, 2025	Continual LearningKnowledge Distillation	CodeCode Available	0
Learning without Isolation: Pathway Protection for Continual Learning	May 24, 2025	Continual LearningGraph Matching	CodeCode Available	0
Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models	May 23, 2025	Continual LearningQuestion Answering	—Unverified	0
The Cell Must Go On: Agar.io for Continual Reinforcement Learning	May 23, 2025	Continual LearningDeep Reinforcement Learning	CodeCode Available	1
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning	May 21, 2025	Continual LearningIncremental Learning	—Unverified	0
Gated Integration of Low-Rank Adaptation for Continual Learning of Language Models	May 21, 2025	Continual Learningparameter-efficient fine-tuning	CodeCode Available	1
Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes	May 21, 2025	Continual LearningEnsemble Learning	CodeCode Available	0
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning	May 20, 2025	Continual LearningContrastive Learning	—Unverified	0
Listen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing	May 20, 2025	class-incremental learningClass Incremental Learning	—Unverified	0
ACU: Analytic Continual Unlearning for Efficient and Exact Forgetting with Privacy Preservation	May 18, 2025	Continual Learning	—Unverified	0
Scalable Strategies for Continual Learning with Replay	May 18, 2025	Continual LearningTask Arithmetic	—Unverified	0
AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data	May 18, 2025	Continual Learning	—Unverified	0
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting	May 17, 2025	class-incremental learningClass Incremental Learning	—Unverified	0
Continuous Subspace Optimization for Continual Learning	May 17, 2025	Continual Learning	—Unverified	0
MINGLE: Mixtures of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging	May 17, 2025	Continual LearningMixture-of-Experts	—Unverified	0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection	May 17, 2025	Anomaly DetectionContinual Learning	—Unverified	0
Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation	May 17, 2025	Continual Learningparameter-efficient fine-tuning	—Unverified	0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection	May 17, 2025	Anomaly DetectionContinual Learning	—Unverified	0
Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis	May 16, 2025	Continual LearningRepresentation Learning	CodeCode Available	3
Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration	May 16, 2025	Continual LearningFederated Learning	—Unverified	0
A Conformal Predictive Measure for Assessing Catastrophic Forgetting	May 15, 2025	Conformal PredictionContinual Learning	—Unverified	0
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence	May 15, 2025	Computational EfficiencyContinual Learning	CodeCode Available	0
ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts	May 15, 2025	Continual LearningLanguage Modeling	CodeCode Available	1
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback	May 15, 2025	Continual LearningContrastive Learning	—Unverified	0
Task-Core Memory Management and Consolidation for Long-term Continual Learning	May 15, 2025	Continual LearningManagement	—Unverified	0
Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging	May 15, 2025	Continual LearningDiagnostic	—Unverified	0
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection	May 14, 2025	class-incremental learningClass Incremental Learning	—Unverified	0
Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning	May 13, 2025	Continual LearningDomain Adaptation	—Unverified	0
Continual Reinforcement Learning via Autoencoder-Driven Task and New Environment Recognition	May 13, 2025	Continual Learning	—Unverified	0
Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis	May 13, 2025	Continual LearningMultiple Instance Learning	—Unverified	0
PrePrompt: Predictive prompting for class incremental learning	May 13, 2025	Classifier calibrationclass-incremental learning	CodeCode Available	1
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer	May 13, 2025	class-incremental learningClass Incremental Learning	—Unverified	0

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All datasets ASC (19 tasks)visual domain decathlon (10 tasks)Cifar100 (20 tasks)Tiny-ImageNet (10tasks)F-CelebA (10 tasks)20Newsgroup (10 tasks)CUBS (Fine-grained 6 Tasks)DSC (10 tasks)Flowers (Fine-grained 6 Tasks)ImageNet (Fine-grained 6 Tasks)Sketch (Fine-grained 6 Tasks)Stanford Cars (Fine-grained 6 Tasks)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Multi-task Learning (MTL; Upper Bound)	F1 - macro	0.88	—	Unverified
2	CTR	F1 - macro	0.84	—	Unverified
3	B-CL	F1 - macro	0.81	—	Unverified
4	LAMOL	F1 - macro	0.81	—	Unverified
5	OWM	F1 - macro	0.79	—	Unverified
6	A-GEM	F1 - macro	0.78	—	Unverified
7	HAT	F1 - macro	0.78	—	Unverified
8	Independent Learning (ONE)	F1 - macro	0.78	—	Unverified
9	KAN	F1 - macro	0.77	—	Unverified
10	Naive Continual Learning (NCL)	F1 - macro	0.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NetTailor	decathlon discipline (Score)	3,744	—	Unverified
2	Depthwise Soft Sharing	decathlon discipline (Score)	3,507	—	Unverified
3	Parallel Res. adapt.	decathlon discipline (Score)	3,412	—	Unverified
4	Depthwise Sharing	decathlon discipline (Score)	3,234	—	Unverified
5	Series Res. adapt.	decathlon discipline (Score)	3,159	—	Unverified
6	Res. adapt. (large)	decathlon discipline (Score)	3,131	—	Unverified
7	DAN	decathlon discipline (Score)	2,851	—	Unverified
8	Piggyback	decathlon discipline (Score)	2,838	—	Unverified
9	Res. adapt. finetune all	decathlon discipline (Score)	2,643	—	Unverified
10	Res. adapt. decay	decathlon discipline (Score)	2,621	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Model Zoo-Continual	Average Accuracy	94.99	—	Unverified
2	ALTA	Average Accuracy	92.98	—	Unverified
3	kNN-CLIP	Average Accuracy	90.8	—	Unverified
4	RMN	Average Accuracy	81	—	Unverified
5	CPG	Average Accuracy	80.9	—	Unverified
6	CondConvContinual	Average Accuracy	77.4	—	Unverified
7	PAENet	Average Accuracy	77.1	—	Unverified
8	CPG-light	Average Accuracy	77	—	Unverified
9	PackNet	Average Accuracy	67.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ALTA-ViTB/16	Average Accuracy	89.8	—	Unverified
2	ALTA-RN50x4	Average Accuracy	84.73	—	Unverified
3	ALTA-RN101	Average Accuracy	83.35	—	Unverified
4	ALTA-RN50	Average Accuracy	81.07	—	Unverified
5	SNCL	Average Accuracy	52.85	—	Unverified
6	DER [buzzega2020dark]	Average Accuracy	51.78	—	Unverified
7	ER[riemer2018learning]	Average Accuracy	48.64	—	Unverified
8	iCaRL [rebuffi2017icarl]	Average Accuracy	31.55	—	Unverified
9	A-GEM [chaudhry2018efficient]	Average Accuracy	25.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAT (CNN backbone)	Acc	0.76	—	Unverified
2	CAT (MLP backbone)	Acc	0.69	—	Unverified
3	EWC	Acc	0.65	—	Unverified
4	HyperNet	Acc	0.6	—	Unverified
5	PathNet	Acc	0.58	—	Unverified
6	HAT	Acc	0.57	—	Unverified
7	RPSNet	Acc	0.55	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CTR	F1 - macro	0.95	—	Unverified
2	HAT	F1 - macro	0.95	—	Unverified
3	CAT	F1 - macro	0.95	—	Unverified
4	B-CL	F1 - macro	0.95	—	Unverified
5	EWC	F1 - macro	0.92	—	Unverified
6	LAMOL	F1 - macro	0.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CondConvContinual	Accuracy	84.26	—	Unverified
2	H$^{2}$	Accuracy	84.1	—	Unverified
3	CPG	Accuracy	83.59	—	Unverified
4	Piggyback	Accuracy	80.5	—	Unverified
5	PackNet	Accuracy	80.41	—	Unverified
6	ProgressiveNet	Accuracy	78.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CTR	F1 - macro	0.89	—	Unverified
2	CAT	F1 - macro	0.87	—	Unverified
3	HAT	F1 - macro	0.86	—	Unverified
4	KAN	F1 - macro	0.81	—	Unverified
5	B-CL	F1 - macro	0.77	—	Unverified
6	EWC	F1 - macro	0.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CondConvContinual	Accuracy	97.16	—	Unverified
2	CPG	Accuracy	96.62	—	Unverified
3	H$^{2}$	Accuracy	94.9	—	Unverified
4	Piggyback	Accuracy	94.77	—	Unverified
5	ProgressiveNet	Accuracy	93.41	—	Unverified
6	PackNet	Accuracy	93.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Piggyback	Accuracy	76.16	—	Unverified
2	ProgressiveNet	Accuracy	76.16	—	Unverified
3	CondConvContinual	Accuracy	76.16	—	Unverified
4	CPG	Accuracy	75.81	—	Unverified
5	PackNet	Accuracy	75.71	—	Unverified
6	H$^{2}$	Accuracy	75.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CondConvContinual	Accuracy	80.77	—	Unverified
2	CPG	Accuracy	80.33	—	Unverified
3	Piggyback	Accuracy	79.91	—	Unverified
4	ProgressiveNet	Accuracy	76.35	—	Unverified
5	H$^{2}$	Accuracy	76.2	—	Unverified
6	PackNet	Accuracy	76.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CPG	Accuracy	92.8	—	Unverified
2	CondConvContinual	Accuracy	92.61	—	Unverified
3	H$^{2}$	Accuracy	90.6	—	Unverified
4	Piggyback	Accuracy	89.62	—	Unverified
5	ProgressiveNet	Accuracy	89.21	—	Unverified
6	PackNet	Accuracy	86.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CondConvContinual	Accuracy	78.32	—	Unverified
2	CPG	Accuracy	77.15	—	Unverified
3	H$^{2}$	Accuracy	75.1	—	Unverified
4	ProgressiveNet	Accuracy	74.94	—	Unverified
5	Piggyback	Accuracy	71.33	—	Unverified
6	PackNet	Accuracy	69.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ALTA-ViTB/16	Average Accuracy	92.85	—	Unverified
2	ALTA-RN50x4	Average Accuracy	84.91	—	Unverified
3	RMN (Resnet)	Average Accuracy	84.9	—	Unverified
4	ALTA-RN101	Average Accuracy	84.77	—	Unverified
5	ALTA-RN50	Average Accuracy	83.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RMN	Accuracy	68.1	—	Unverified
2	CondConvContinual	Accuracy	61.32	—	Unverified
3	CCGN	Accuracy	35.24	—	Unverified
4	DGMw	Accuracy	17.82	—	Unverified
5	DGMa	Accuracy	15.16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RMN	Average Accuracy	97.99	—	Unverified
2	Model Zoo-Continual	Average Accuracy	97.71	—	Unverified
3	CODE-CL	Average Accuracy	96.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TAG-RMSProp	Accuracy	62.59	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CODE-CL	Average Accuracy	93.32	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TEST	1:3 Accuracy	2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TAG-RMSProp	Average Accuracy	62.79	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IBM	Accuracy	82.69	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IBM	Accuracy	88.15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Model Zoo-Continual	Average Accuracy	84.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TAG-RMSProp	Accuracy	61.58	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CODE-CL	Average Accuracy	68.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TAG-RMSProp	Accuracy	57.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IBM	Accuracy	53.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MRM	Acc	78.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Model Zoo-Continual	Average Accuracy	99.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CODE-CL	Average Accuracy	77.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	H$^{2}$	Top 1 Accuracy %	97.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	H$^{2}$	Top 1 Accuracy %	99.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IBM	Accuracy	52.38	—	Unverified