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 651–700 of 2644 papers

Title	Date	Tasks	Status	Score
Improving Representational Continuity via Continued Pretraining	Feb 26, 2023	Continual LearningRepresentation Learning	CodeCode Available	5
Artificial Neuronal Ensembles with Learned Context Dependent Gating	Jan 17, 2023	Continual LearningPermuted-MNIST	CodeCode Available	5
Class-Incremental Learning based on Label Generation	Jun 22, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	5
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks	Oct 11, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	5
In-context Continual Learning Assisted by an External Continual Learner	Dec 20, 2024	Continual LearningIn-Context Learning	CodeCode Available	5
Class-Incremental Experience Replay for Continual Learning under Concept Drift	Apr 24, 2021	Continual Learningvalid	CodeCode Available	5
Improving Online Continual Learning Performance and Stability with Temporal Ensembles	Jun 29, 2023	Continual Learning	CodeCode Available	5
Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual Learning	Jul 5, 2025	Continual LearningData Poisoning	CodeCode Available	5
Improving Meta-Continual Learning Representations with Representation Replay	Sep 29, 2021	Continual LearningMeta-Learning	CodeCode Available	5
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection	Sep 20, 2022	Continual LearningContinual Semantic Segmentation	CodeCode Available	5
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence	May 15, 2025	Computational EfficiencyContinual Learning	CodeCode Available	5
Integrating Curricula with Replays: Its Effects on Continual Learning	Jul 8, 2023	Continual LearningTransfer Learning	CodeCode Available	5
Class Gradient Projection For Continual Learning	Nov 25, 2023	Continual LearningContrastive Learning	CodeCode Available	5
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift	May 30, 2023	Continual Learning	CodeCode Available	5
Evaluating Online Continual Learning with CALM	Apr 7, 2020	Continual LearningLanguage Modelling	CodeCode Available	5
Class-Adaptive Sampling Policy for Efficient Continual Learning	Nov 27, 2023	Continual Learning	CodeCode Available	5
CITB: A Benchmark for Continual Instruction Tuning	Oct 23, 2023	Continual Learning	CodeCode Available	5
A Protocol for Continual Explanation of SHAP	Jun 12, 2023	Continual Learning	CodeCode Available	5
Improving Data-aware and Parameter-aware Robustness for Continual Learning	May 27, 2024	Continual Learning	CodeCode Available	5
Chunking: Continual Learning is not just about Distribution Shift	Oct 3, 2023	ChunkingContinual Learning	CodeCode Available	5
LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning	Oct 1, 2024	Continual Learning	CodeCode Available	5
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual Learning	Oct 4, 2023	Continual Learningimage-classification	CodeCode Available	5
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts	Jul 9, 2025	Continual LearningIncremental Learning	CodeCode Available	5
ICICLE: Interpretable Class Incremental Continual Learning	Mar 14, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	5
Tuned Compositional Feature Replays for Efficient Stream Learning	Apr 6, 2021	Continual LearningImage Classification	CodeCode Available	5
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning	Jun 4, 2021	Continual Learning	CodeCode Available	5
A Probabilistic Framework for Modular Continual Learning	Jun 11, 2023	Continual Learning	CodeCode Available	5
Dealing with Cross-Task Class Discrimination in Online Continual Learning	May 24, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	5
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting	Mar 26, 2025	class-incremental learningClass Incremental Learning	CodeCode Available	5
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning	Apr 28, 2024	Continual LearningRepresentation Learning	CodeCode Available	5
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness	Mar 22, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	5
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification	Feb 19, 2024	ClassificationContinual Learning	CodeCode Available	5
Learning multiple visual domains with residual adapters	May 22, 2017	Continual Learning	CodeCode Available	5
Hierarchically Structured Meta-learning	May 13, 2019	ClusteringContinual Learning	CodeCode Available	5
Hierarchically Structured Task-Agnostic Continual Learning	Nov 14, 2022	Continual Learningreinforcement-learning	CodeCode Available	5
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?	Aug 14, 2024	Continual LearningGaussian Processes	CodeCode Available	5
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning	May 1, 2023	ClusteringContinual Learning	CodeCode Available	5
Centroids Matching: an efficient Continual Learning approach operating in the embedding space	Aug 3, 2022	Continual LearningIncremental Learning	CodeCode Available	5
Centroid Distance Distillation for Effective Rehearsal in Continual Learning	Mar 6, 2023	Continual Learning	CodeCode Available	5
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation	Dec 21, 2022	Continual Learning	CodeCode Available	5
How Do Human Users Teach a Continual Learning Robot in Repeated Interactions?	Jun 30, 2023	Continual Learning	CodeCode Available	5
CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation	Jan 17, 2024	Continual LearningDomain Adaptation	CodeCode Available	5
An NMF-Based Building Block for Interpretable Neural Networks With Continual Learning	Nov 20, 2023	Continual Learning	CodeCode Available	5
Helpful or Harmful: Inter-Task Association in Continual Learning	Oct 23, 2022	Class Incremental LearningContinual Learning	CodeCode Available	5
An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities	Sep 16, 2024	Continual LearningEEG	CodeCode Available	5
Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning	Nov 26, 2023	Continual Learning	CodeCode Available	5
Latent Spectral Regularization for Continual Learning	Jan 9, 2023	Continual LearningIncremental Learning	CodeCode Available	5
Adaptive Visual Scene Understanding: Incremental Scene Graph Generation	Oct 2, 2023	BenchmarkingContinual Learning	CodeCode Available	5
Class incremental learning with probability dampening and cascaded gated classifier	Feb 2, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	5
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems	Aug 23, 2023	Continual LearningDeep Reinforcement Learning	CodeCode Available	5

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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