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 2251–2300 of 2644 papers

Title	Date	Tasks	Status
Exploring the Stability Gap in Continual Learning: The Role of the Classification Head	Nov 6, 2024	AttributeContinual Learning	CodeCode Available
Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning	Jun 6, 2023	Continual LearningRepresentation Learning	CodeCode Available
An Investigation of the Combination of Rehearsal and Knowledge Distillation in Continual Learning for Spoken Language Understanding	Nov 15, 2022	class-incremental learningClass Incremental Learning	CodeCode Available
Learning System Dynamics without Forgetting	Jun 30, 2024	Continual LearningTrajectory Prediction	CodeCode Available
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning	Mar 28, 2023	Continual LearningKnowledge Distillation	CodeCode Available
Exploring the Evolution of Hidden Activations with Live-Update Visualization	May 24, 2024	Continual Learning	CodeCode Available
Learning to Continually Learn Rapidly from Few and Noisy Data	Mar 6, 2021	Continual LearningMeta-Learning	CodeCode Available
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge Transfer	Feb 27, 2025	Continual LearningTransfer Learning	CodeCode Available
Exploring Data Redundancy in Real-world Image Classification through Data Selection	Jun 25, 2023	Active LearningContinual Learning	CodeCode Available
Exploring Continual Learning of Compositional Generalization in NLI	Mar 7, 2024	Continual LearningNatural Language Inference	CodeCode Available
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning	Jul 16, 2025	Continual LearningGeneral Knowledge	CodeCode Available
Calibration of Continual Learning Models	Apr 11, 2024	Continual Learning	CodeCode Available
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking	Jul 17, 2021	Continual LearningNetwork Pruning	CodeCode Available
Learning to learn with backpropagation of Hebbian plasticity	Sep 8, 2016	Continual LearningLifelong learning	CodeCode Available
PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery	Jul 26, 2024	Continual LearningRepresentation Learning	CodeCode Available
Exploring Continual Learning for Code Generation Models	Jul 5, 2023	Code GenerationContinual Learning	CodeCode Available
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning	Apr 8, 2022	Computational EfficiencyContinual Learning	CodeCode Available
Prompt Customization for Continual Learning	Apr 28, 2024	Continual LearningIncremental Learning	CodeCode Available
Learning to Predict Gradients for Semi-Supervised Continual Learning	Jan 23, 2022	Continual Learning	CodeCode Available
Explaining How Deep Neural Networks Forget by Deep Visualization	May 3, 2020	Continual LearningExplainable artificial intelligence	CodeCode Available
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning	May 19, 2022	Continual LearningTransfer Learning	CodeCode Available
Learning to Prompt Knowledge Transfer for Open-World Continual Learning	Dec 22, 2023	Continual LearningTransfer Learning	CodeCode Available
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning	Apr 5, 2019	Continual Learning	CodeCode Available
Prompt-Driven Continual Graph Learning	Feb 10, 2025	Continual LearningGraph Learning	CodeCode Available
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning	Jun 4, 2021	Continual Learning	CodeCode Available
PromptFusion: Decoupling Stability and Plasticity for Continual Learning	Mar 13, 2023	class-incremental learningClass Incremental Learning	CodeCode Available
Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites	Jun 14, 2022	Continual Learning	CodeCode Available
Ex-Model: Continual Learning from a Stream of Trained Models	Dec 13, 2021	Continual Learning	CodeCode Available
Prompting Continual Person Search	Oct 25, 2024	AttributeContinual Learning	CodeCode Available
Exclusive Supermask Subnetwork Training for Continual Learning	Oct 18, 2022	Continual LearningText Classification	CodeCode Available
EVCL: Elastic Variational Continual Learning with Weight Consolidation	Jun 23, 2024	Continual LearningIncremental Learning	CodeCode Available
Learning without Isolation: Pathway Protection for Continual Learning	May 24, 2025	Continual LearningGraph Matching	CodeCode Available
Continual learning for surface defect segmentation by subnetwork creation and selection	Dec 8, 2023	Continual LearningLifelong learning	CodeCode Available
Calibrating CNNs for Lifelong Learning	Dec 1, 2020	Continual LearningLifelong learning	CodeCode Available
Continual Learning for Sentence Representations Using Conceptors	Apr 18, 2019	Continual LearningSemantic Textual Similarity	CodeCode Available
Learn the Time to Learn: Replay Scheduling in Continual Learning	Sep 18, 2022	Continual LearningScheduling	CodeCode Available
AGALE: A Graph-Aware Continual Learning Evaluation Framework	Jun 3, 2024	Continual LearningGraph Learning	CodeCode Available
Semi-Discriminative Representation Loss for Online Continual Learning	Jun 19, 2020	Continual LearningDiversity	CodeCode Available
Adversarial Training of Variational Auto-encoders for Continual Zero-shot Learning(A-CZSL)	Feb 7, 2021	Continual LearningGeneralized Zero-Shot Learning	CodeCode Available
Tiny Robotics Dataset and Benchmark for Continual Object Detection	Sep 24, 2024	Autonomous NavigationContinual Learning	CodeCode Available
TinySubNets: An efficient and low capacity continual learning strategy	Dec 14, 2024	Continual LearningQuantization	CodeCode Available
Variational Prototype Replays for Continual Learning	May 23, 2019	Continual LearningGeneral Classification	CodeCode Available
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning	Nov 13, 2023	Computational EfficiencyContinual Learning	CodeCode Available
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning	Oct 6, 2024	Continual Learning	CodeCode Available
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems	Mar 22, 2023	Continual LearningTransfer Learning	CodeCode Available
Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning	Feb 14, 2023	Continual LearningLifelong learning	CodeCode Available
Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL	Nov 20, 2024	Continual LearningIn-Context Learning	CodeCode Available
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms	Nov 19, 2023	Continual LearningMeta-Learning	CodeCode Available
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks	Sep 23, 2021	3D Object RecognitionContinual Learning	CodeCode Available
Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference	Jul 19, 2024	class-incremental learningClass Incremental Learning	CodeCode Available

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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