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 2501–2550 of 2644 papers

Title	Date	Tasks	Status
NetTailor: Tuning the Architecture, Not Just the Weights	Jun 29, 2019	Continual LearningObject Recognition	CodeCode Available
ContCap: A scalable framework for continual image captioning	Sep 19, 2019	Continual LearningImage Captioning	CodeCode Available
Neural Architecture for Online Ensemble Continual Learning	Nov 27, 2022	Continual Learning	CodeCode Available
Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping	Feb 22, 2021	Continual Learning	CodeCode Available
Dealing with Synthetic Data Contamination in Online Continual Learning	Nov 21, 2024	Continual LearningImage Generation	CodeCode Available
Consistency is the key to further mitigating catastrophic forgetting in continual learning	Jul 11, 2022	Continual LearningSelf-Supervised Learning	CodeCode Available
Task agnostic continual learning with Pairwise layer architecture	May 22, 2024	Continual Learningimage-classification	CodeCode Available
How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation	Feb 4, 2021	Continual LearningLifelong learning	CodeCode Available
Condensed Composite Memory Continual Learning	Feb 19, 2021	Continual Learning	CodeCode Available
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review	Mar 31, 2025	Continual LearningGraph Learning	CodeCode Available
Compression-aware Continual Learning using Singular Value Decomposition	Sep 3, 2020	Continual LearningModel Compression	CodeCode Available
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective	Jun 4, 2025	Continual Learning	CodeCode Available
Data Stream Sampling with Fuzzy Task Boundaries and Noisy Labels	Apr 7, 2024	Continual LearningFairness	CodeCode Available
Dataset Distillation using Neural Feature Regression	Jun 1, 2022	Continual LearningDataset Distillation	CodeCode Available
Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks	Oct 7, 2023	Continual LearningDiagnostic	CodeCode Available
Dataset Condensation with Differentiable Siamese Augmentation	Feb 16, 2021	Continual LearningData Augmentation	CodeCode Available
Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data	Jun 7, 2024	class-incremental learningClass Incremental Learning	CodeCode Available
New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning	Feb 16, 2023	class-incremental learningClass Incremental Learning	CodeCode Available
[Re] Training Binary Neural Networks using the Bayesian Learning Rule	Jan 31, 2021	Continual LearningSemantic Segmentation	CodeCode Available
DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free Continual Learning	Feb 17, 2025	Continual Learning	CodeCode Available
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions	Sep 14, 2020	BenchmarkingContinual Learning	CodeCode Available
Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks	May 8, 2024	Continual Learning	CodeCode Available
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory	Feb 6, 2025	Continual LearningQuestion Answering	CodeCode Available
BACS: Background Aware Continual Semantic Segmentation	Apr 19, 2024	Autonomous DrivingContinual Learning	CodeCode Available
Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach	Jul 13, 2022	Continual Learning	CodeCode Available
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning	Sep 22, 2019	Continual LearningDeep Reinforcement Learning	CodeCode Available
Variational Bayes for Federated Continual Learning	May 23, 2024	Continual LearningFederated Learning	CodeCode Available
Bayesian Structure Adaptation for Continual Learning	Dec 8, 2019	Continual Learning	CodeCode Available
Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery	Jul 22, 2023	Continual LearningScene Understanding	CodeCode Available
Non-Uniform Memory Sampling in Experience Replay	Feb 16, 2025	Continual Learning	CodeCode Available
Task-Aware Information Routing from Common Representation Space in Lifelong Learning	Feb 14, 2023	Continual LearningLifelong learning	CodeCode Available
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning	May 26, 2025	Continual LearningMissing Labels	CodeCode Available
Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning	Jan 29, 2022	class-incremental learningClass Incremental Learning	CodeCode Available
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features	Aug 11, 2023	Continual LearningEmotion Recognition	CodeCode Available
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets	Jul 22, 2022	Continual LearningTransfer Learning	CodeCode Available
Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation	Mar 31, 2020	ColorizationContinual Learning	CodeCode Available
TaskDrop: A Competitive Baseline for Continual Learning of Sentiment Classification	Nov 5, 2021	ClassificationContinual Learning	CodeCode Available
CUCL: Codebook for Unsupervised Continual Learning	Nov 25, 2023	Continual LearningDiversity	CodeCode Available
On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networks	Jul 11, 2018	Continual Learning	CodeCode Available
Revisiting Supervision for Continual Representation Learning	Nov 22, 2023	Continual LearningRepresentation Learning	CodeCode Available
Adiabatic replay for continual learning	Mar 23, 2023	Continual Learning	CodeCode Available
Rewiring Neurons in Non-Stationary Environments	Sep 21, 2023	Continual Learningreinforcement-learning	CodeCode Available
Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision	Sep 3, 2021	Continual LearningContrastive Learning	CodeCode Available
CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings	Nov 15, 2021	Continual LearningFew-Shot Learning	CodeCode Available
Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering	Sep 30, 2022	Continual LearningQuestion Answering	CodeCode Available
CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning	Jan 13, 2025	class-incremental learningClass Incremental Learning	CodeCode Available
Task-Free Continual Learning	Dec 10, 2018	Continual LearningFace Recognition	CodeCode Available
One-Shot Domain Incremental Learning	Mar 25, 2024	Continual LearningIncremental Learning	CodeCode Available
Robust Deep Reinforcement Learning Scheduling via Weight Anchoring	Apr 20, 2023	Continual LearningDeep Reinforcement Learning	CodeCode Available
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning	Nov 18, 2023	AllContinual 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