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 1001–1050 of 2644 papers

Title	Date	Tasks	Status	Score
Hierarchically Structured Task-Agnostic Continual Learning	Nov 14, 2022	Continual Learningreinforcement-learning	CodeCode Available	5
Continual Learning for Automated Audio Captioning Using The Learning Without Forgetting Approach	Jul 16, 2021	Audio captioningContinual Learning	CodeCode Available	5
Behaviour Distillation	Jun 21, 2024	Continual Learningcontinuous-control	CodeCode Available	5
Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach	Jul 13, 2022	Continual Learning	CodeCode Available	5
Helpful or Harmful: Inter-Task Association in Continual Learning	Oct 23, 2022	Class Incremental LearningContinual Learning	CodeCode Available	5
Alleviating Representational Shift for Continual Fine-tuning	Apr 22, 2022	Continual Learning	CodeCode Available	5
Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning	Nov 26, 2023	Continual Learning	CodeCode Available	5
HAT-CL: A Hard-Attention-to-the-Task PyTorch Library for Continual Learning	Jul 18, 2023	Continual LearningHard Attention	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
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning	Apr 23, 2024	Continual Learning	CodeCode Available	5
In-context Continual Learning Assisted by an External Continual Learner	Dec 20, 2024	Continual LearningIn-Context Learning	CodeCode Available	5
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis	Oct 29, 2024	Continual LearningImage Generation	CodeCode Available	5
Gradient Episodic Memory for Continual Learning	Jun 26, 2017	Continual LearningIncremental Learning	CodeCode Available	5
Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection	Apr 27, 2023	3D Object DetectionAll	CodeCode Available	5
Graceful task adaptation with a bi-hemispheric RL agent	Jul 16, 2024	Continual Learning	CodeCode Available	5
Algorithmic insights on continual learning from fruit flies	Jul 15, 2021	Continual LearningLifelong learning	CodeCode Available	5
Generative vs. Discriminative: Rethinking The Meta-Continual Learning	Dec 1, 2021	Continual LearningMeta-Learning	CodeCode Available	5
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning	Dec 27, 2023	Continual Learninggraph construction	CodeCode Available	5
Gradient Correlation Subspace Learning against Catastrophic Forgetting	Mar 4, 2024	Continual Learning	CodeCode Available	5
Dynamic VAEs with Generative Replay for Continual Zero-shot Learning	Apr 26, 2021	Continual LearningZero-Shot Learning	CodeCode Available	5
Growing a Brain with Sparsity-Inducing Generation for Continual Learning	Jan 1, 2023	Action RecognitionContinual Learning	CodeCode Available	5
Continual Learning Approaches for Anomaly Detection	Dec 21, 2022	Anomaly DetectionContinual Learning	CodeCode Available	5
Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery	Aug 23, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	5
Generative Models from the perspective of Continual Learning	Dec 21, 2018	Continual LearningImage Generation	CodeCode Available	5
Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes	May 21, 2025	Continual LearningEnsemble Learning	CodeCode Available	5
Context selectivity with dynamic availability enables lifelong continual learning	Jun 2, 2023	Continual LearningLifelong learning	CodeCode Available	5
Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate	Jul 8, 2025	Continual LearningMixture-of-Experts	CodeCode Available	5
Bayesian Continual Learning via Spiking Neural Networks	Aug 29, 2022	Continual LearningManagement	CodeCode Available	5
A continual learning survey: Defying forgetting in classification tasks	Sep 18, 2019	ClassificationContinual Learning	CodeCode Available	5
From Static to Dynamic: A Continual Learning Framework for Large Language Models	Oct 22, 2023	Continual Learning	CodeCode Available	5
Functional Regularisation for Continual Learning with Gaussian Processes	Jan 31, 2019	Bayesian InferenceContinual Learning	CodeCode Available	5
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework	Nov 4, 2024	Continual LearningContrastive Learning	CodeCode Available	5
From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19	Oct 16, 2024	Continual Learning	CodeCode Available	5
Batch Model Consolidation: A Multi-Task Model Consolidation Framework	May 25, 2023	Continual Learningimage-classification	CodeCode Available	5
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning	Mar 16, 2023	BenchmarkingContinual Learning	CodeCode Available	5
Function-space Parameterization of Neural Networks for Sequential Learning	Mar 16, 2024	Continual LearningGaussian Processes	CodeCode Available	5
Continual Gradient Low-Rank Projection Fine-Tuning for LLMs	Jul 3, 2025	Continual Learning	CodeCode Available	5
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts	Mar 13, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	5
Expanding continual few-shot learning benchmarks to include recognition of specific instances	Aug 26, 2022	continual few-shot learningContinual Learning	CodeCode Available	5
Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks	Oct 7, 2023	Continual LearningDiagnostic	CodeCode Available	5
Flatness-aware Sequential Learning Generates Resilient Backdoors	Jul 20, 2024	Continual Learning	CodeCode Available	5
F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation	Apr 7, 2024	Continual LearningMachine Translation	CodeCode Available	5
Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning	Sep 28, 2024	Continual LearningMaximum Separation	CodeCode Available	5
Few-Shot Continual Learning via Flat-to-Wide Approaches	Jun 26, 2023	Continual LearningData Augmentation	CodeCode Available	5
Few-shot Tuning of Foundation Models for Class-incremental Learning	May 26, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	5
BACS: Background Aware Continual Semantic Segmentation	Apr 19, 2024	Autonomous DrivingContinual Learning	CodeCode Available	5
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments	Mar 12, 2024	Continual LearningFederated Learning	CodeCode Available	5
Learning to Learn without Forgetting using Attention	Aug 6, 2024	Continual LearningMeta-Learning	CodeCode Available	5
Federated Continual Graph Learning	Nov 28, 2024	Continual LearningGraph Learning	CodeCode Available	5
Continual Dialogue State Tracking via Reason-of-Select Distillation	Aug 19, 2024	Continual LearningDialogue State Tracking	CodeCode Available	5

Show:10 25 50

← PrevPage 21 of 53Next →

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	ProgressiveNet	Accuracy	76.16	—	Unverified
2	Piggyback	Accuracy	76.16	—	Unverified
3	CondConvContinual	Accuracy	76.16	—	Unverified
4	CPG	Accuracy	75.81	—	Unverified
5	H$^{2}$	Accuracy	75.71	—	Unverified
6	PackNet	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