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 201–250 of 2644 papers

Title	Date	Tasks	Status	Hype	Score
DataDAM: Efficient Dataset Distillation with Attention Matching	Sep 29, 2023	Continual LearningDataset Distillation	CodeCode Available	1	5
Dataset Condensation with Contrastive Signals	Feb 7, 2022	AttributeContinual Learning	CodeCode Available	1	5
BiasPruner: Debiased Continual Learning for Medical Image Classification	Jul 11, 2024	ClassificationContinual Learning	CodeCode Available	1	5
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning	Oct 14, 2021	Continual LearningManagement	CodeCode Available	1	5
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning	Nov 23, 2022	Continual LearningNovel Concepts	CodeCode Available	1	5
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal	Sep 28, 2022	Continual LearningReinforcement Learning (RL)	CodeCode Available	1	5
CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning	Jul 22, 2024	Continual LearningIncremental Learning	CodeCode Available	1	5
A soft nearest-neighbor framework for continual semi-supervised learning	Dec 9, 2022	Continual Learning	CodeCode Available	1	5
ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation	Jul 23, 2020	Continual LearningSession-Based Recommendations	CodeCode Available	1	5
Compacting, Picking and Growing for Unforgetting Continual Learning	Oct 15, 2019	Age And Gender ClassificationContinual Learning	CodeCode Available	1	5
Compositional Language Continual Learning	May 1, 2020	Continual LearningMachine Translation	CodeCode Available	1	5
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues	Sep 18, 2023	Continual LearningContrastive Learning	CodeCode Available	1	5
Disentangle-based Continual Graph Representation Learning	Oct 6, 2020	Continual LearningGraph Embedding	CodeCode Available	1	5
Distilled Replay: Overcoming Forgetting through Synthetic Samples	Mar 29, 2021	Continual Learning	CodeCode Available	1	5
Action Flow Matching for Continual Robot Learning	Apr 25, 2025	Continual Learning	CodeCode Available	1	5
Do Your Best and Get Enough Rest for Continual Learning	Mar 24, 2025	Continual LearningIncremental Learning	CodeCode Available	1	5
Conditional Channel Gated Networks for Task-Aware Continual Learning	Mar 31, 2020	Continual Learning	CodeCode Available	1	5
ConPET: Continual Parameter-Efficient Tuning for Large Language Models	Sep 26, 2023	Continual Learning	CodeCode Available	1	5
Bilevel Continual Learning	Jul 30, 2020	Bilevel OptimizationContinual Learning	CodeCode Available	1	5
Consistent Prompting for Rehearsal-Free Continual Learning	Mar 13, 2024	Continual Learning	CodeCode Available	1	5
Continual Learning of Diffusion Models with Generative Distillation	Nov 23, 2023	Continual LearningDenoising	CodeCode Available	1	5
Continual Action Assessment via Task-Consistent Score-Discriminative Feature Distribution Modeling	Sep 29, 2023	Action AssessmentAction Quality Assessment	CodeCode Available	1	5
Continual atlas-based segmentation of prostate MRI	Nov 1, 2023	Continual Learningimage-classification	CodeCode Available	1	5
Continual BERT: Continual Learning for Adaptive Extractive Summarization of COVID-19 Literature	Jul 7, 2020	Continual LearningExtractive Summarization	CodeCode Available	1	5
A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application	Oct 22, 2023	Continual LearningContinual Semantic Segmentation	CodeCode Available	1	5
Efficiency 360: Efficient Vision Transformers	Feb 16, 2023	Continual LearningFairness	CodeCode Available	1	5
Activation-Informed Merging of Large Language Models	Feb 4, 2025	Computational EfficiencyContinual Learning	CodeCode Available	1	5
Asynchronous Federated Continual Learning	Apr 7, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix	Oct 7, 2020	Continual Learning	CodeCode Available	1	5
Efficient parametrization of multi-domain deep neural networks	Mar 27, 2018	Continual LearningTransfer Learning	CodeCode Available	1	5
A Theoretical Study on Solving Continual Learning	Nov 4, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models	Jun 27, 2024	Continual LearningIncremental Learning	CodeCode Available	1	5
Advancing Prompt-Based Methods for Replay-Independent General Continual Learning	Mar 2, 2025	Continual Learning	CodeCode Available	1	5
Engineering flexible machine learning systems by traversing functionally-invariant paths	Apr 30, 2022	Adversarial RobustnessContinual Learning	CodeCode Available	1	5
Adversarial Continual Learning	Mar 21, 2020	Continual Learningimage-classification	CodeCode Available	1	5
Continual Evidential Deep Learning for Out-of-Distribution Detection	Sep 6, 2023	Continual LearningDeep Learning	CodeCode Available	1	5
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation	Jul 19, 2021	Continual LearningContinual Semantic Segmentation	CodeCode Available	1	5
Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation	Apr 15, 2023	Continual LearningDomain Adaptation	CodeCode Available	1	5
Continual Hippocampus Segmentation with Transformers	Apr 17, 2022	Continual LearningHippocampus	CodeCode Available	1	5
Continual Learning at the Edge: Real-Time Training on Smartphone Devices	May 24, 2021	Continual Learning	CodeCode Available	1	5
Fast Trainable Projection for Robust Fine-Tuning	Oct 29, 2023	Continual Learning	CodeCode Available	1	5
Continual Learning and Private Unlearning	Mar 24, 2022	Continual Learning	CodeCode Available	1	5
Continual Learning by Modeling Intra-Class Variation	Oct 11, 2022	Continual Learning	CodeCode Available	1	5
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation	Oct 13, 2022	Class-Incremental Semantic SegmentationContinual Learning	CodeCode Available	1	5
Continual Learning Based on OOD Detection and Task Masking	Mar 17, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
FedJudge: Federated Legal Large Language Model	Sep 15, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
A Unified and General Framework for Continual Learning	Mar 20, 2024	Continual Learning	CodeCode Available	1	5
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm	Oct 18, 2023	Continual LearningIncremental Learning	CodeCode Available	1	5
Continual Learning for Abdominal Multi-Organ and Tumor Segmentation	Jun 1, 2023	Continual LearningOrgan Segmentation	CodeCode Available	1	5
Continual Learning on Dynamic Graphs via Parameter Isolation	May 23, 2023	Continual LearningGraph Learning	CodeCode Available	1	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