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 451–500 of 2644 papers

Title	Date	Tasks	Status	Hype	Score
Avalanche: an End-to-End Library for Continual Learning	Apr 1, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion	Nov 22, 2021	Class Incremental LearningContinual Learning	CodeCode Available	1	5
Rethinking Experience Replay: a Bag of Tricks for Continual Learning	Oct 12, 2020	Continual Learning	CodeCode Available	1	5
Rethinking Few-shot Class-incremental Learning: Learning from Yourself	Jul 10, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning	Apr 21, 2022	Continual Learning	CodeCode Available	1	5
Co^2L: Contrastive Continual Learning	Jun 28, 2021	Continual LearningContrastive Learning	CodeCode Available	1	5
Co2L: Contrastive Continual Learning	Jan 1, 2021	Continual LearningContrastive Learning	CodeCode Available	1	5
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro	Aug 11, 2023	Continual LearningEdge-computing	CodeCode Available	1	5
FoCL: Feature-Oriented Continual Learning for Generative Models	Mar 9, 2020	Continual LearningIncremental Learning	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
CoDeC: Communication-Efficient Decentralized Continual Learning	Mar 27, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning	Nov 21, 2024	Continual LearningImage Classification	CodeCode Available	1	5
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation	Mar 17, 2023	Continual LearningDepth Estimation	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
Efficient parametrization of multi-domain deep neural networks	Mar 27, 2018	Continual LearningTransfer Learning	CodeCode Available	1	5
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning	Mar 15, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Advancing Prompt-Based Methods for Replay-Independent General Continual Learning	Mar 2, 2025	Continual Learning	CodeCode Available	1	5
Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting	Oct 1, 2024	Continual LearningLanguage Modeling	CodeCode Available	1	5
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model	Jul 4, 2025	Continual LearningImage Segmentation	CodeCode Available	1	5
Forget-free Continual Learning with Winning Subnetworks	Jul 1, 2022	Continual Learning	CodeCode Available	1	5
Energy-Based Models for Continual Learning	Nov 24, 2020	Continual Learning	CodeCode Available	1	5
ScrollNet: Dynamic Weight Importance for Continual Learning	Aug 31, 2023	Continual Learning	CodeCode Available	1	5
Compacting, Picking and Growing for Unforgetting Continual Learning	Oct 15, 2019	Age And Gender ClassificationContinual Learning	CodeCode Available	1	5
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation	Jul 19, 2021	Continual LearningContinual Semantic Segmentation	CodeCode Available	1	5
Evaluating Logical Generalization in Graph Neural Networks	Mar 14, 2020	Continual LearningDiversity	CodeCode Available	1	5
Compositional Language Continual Learning	May 1, 2020	Continual LearningMachine Translation	CodeCode Available	1	5
Essentials for Class Incremental Learning	Feb 18, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Automating Continual Learning	Dec 1, 2023	Continual Learningimage-classification	CodeCode Available	1	5
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision	Jun 29, 2022	Continual LearningGPU	CodeCode Available	1	5
Computationally Budgeted Continual Learning: What Does Matter?	Mar 20, 2023	Continual LearningGPU	CodeCode Available	1	5
SequeL: A Continual Learning Library in PyTorch and JAX	Apr 21, 2023	Continual Learning	CodeCode Available	1	5
Automatic Recall Machines: Internal Replay, Continual Learning and the Brain	Jun 22, 2020	Continual Learning	CodeCode Available	1	5
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model	Mar 9, 2023	AttributeContinual Learning	CodeCode Available	1	5
Exploring Example Influence in Continual Learning	Sep 25, 2022	Continual Learning	CodeCode Available	1	5
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning	Apr 28, 2020	Class Incremental LearningContinual Learning	CodeCode Available	1	5
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING	Dec 7, 2020	Computational EfficiencyContinual Learning	CodeCode Available	1	5
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	Jun 18, 2019	Active LearningContinual 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
Conditional Channel Gated Networks for Task-Aware Continual Learning	Mar 31, 2020	Continual Learning	CodeCode Available	1	5
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning	Sep 25, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Fast Trainable Projection for Robust Fine-Tuning	Oct 29, 2023	Continual Learning	CodeCode Available	1	5
AFEC: Active Forgetting of Negative Transfer in Continual Learning	Oct 23, 2021	Continual LearningTransfer Learning	CodeCode Available	1	5
StableFusion: Continual Video Retrieval via Frame Adaptation	Mar 13, 2025	Continual LearningMixture-of-Experts	CodeCode Available	1	5
A Unified and General Framework for Continual Learning	Mar 20, 2024	Continual Learning	CodeCode Available	1	5
ConPET: Continual Parameter-Efficient Tuning for Large Language Models	Sep 26, 2023	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
FedJudge: Federated Legal Large Language Model	Sep 15, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
Consistent Prompting for Rehearsal-Free Continual Learning	Mar 13, 2024	Continual Learning	CodeCode Available	1	5
ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis	Aug 25, 2023	Continual LearningPrognosis	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