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 51–100 of 2644 papers

Title	Date	Tasks	Status	Hype	Score
Generative replay with feedback connections as a general strategy for continual learning	Sep 27, 2018	Continual LearningLifelong learning	CodeCode Available	2	5
Maintaining Plasticity in Deep Continual Learning	Jun 23, 2023	Binary ClassificationContinual Learning	CodeCode Available	2	5
KAN or MLP: A Fairer Comparison	Jul 23, 2024	Continual Learning	CodeCode Available	2	5
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception	Apr 5, 2022	Continual LearningDenoising	CodeCode Available	2	5
Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation	Nov 8, 2024	Continual Learning	CodeCode Available	2	5
A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application	Oct 22, 2023	Continual LearningContinual Semantic Segmentation	CodeCode Available	1	5
Co^2L: Contrastive Continual Learning	Jun 28, 2021	Continual LearningContrastive 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
Co2L: Contrastive Continual Learning	Jan 1, 2021	Continual LearningContrastive 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
CLoG: Benchmarking Continual Learning of Image Generation Models	Jun 7, 2024	BenchmarkingContinual Learning	CodeCode Available	1	5
Asynchronous Federated Continual Learning	Apr 7, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
CLR: Channel-wise Lightweight Reprogramming for Continual Learning	Jul 21, 2023	Continual Learningimage-classification	CodeCode Available	1	5
Activation-Informed Merging of Large Language Models	Feb 4, 2025	Computational EfficiencyContinual Learning	CodeCode Available	1	5
A soft nearest-neighbor framework for continual semi-supervised learning	Dec 9, 2022	Continual Learning	CodeCode Available	1	5
CL-MASR: A Continual Learning Benchmark for Multilingual ASR	Oct 25, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning	Apr 21, 2022	Continual Learning	CodeCode Available	1	5
CoDeC: Communication-Efficient Decentralized Continual Learning	Mar 27, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability	Oct 7, 2021	Continual Learning	CodeCode Available	1	5
Action Flow Matching for Continual Robot Learning	Apr 25, 2025	Continual Learning	CodeCode Available	1	5
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks	Jun 18, 2022	Continual LearningTransfer Learning	CodeCode Available	1	5
ARCADe: A Rapid Continual Anomaly Detector	Aug 10, 2020	Anomaly Detectioncontinual anomaly detection	CodeCode Available	1	5
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?	Oct 28, 2020	Continual LearningOrgan Segmentation	CodeCode Available	1	5
Class-Incremental Learning with Generative Classifiers	Apr 20, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
CLIP model is an Efficient Continual Learner	Oct 6, 2022	Continual LearningIncremental Learning	CodeCode Available	1	5
A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks	Sep 20, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision	Jan 1, 2023	Continual LearningIncremental Learning	CodeCode Available	1	5
Class-Incremental Grouping Network for Continual Audio-Visual Learning	Sep 11, 2023	audio-visual learningclass-incremental learning	CodeCode Available	1	5
Anytime Continual Learning for Open Vocabulary Classification	Sep 13, 2024	ClassificationContinual Learning	CodeCode Available	1	5
CLAD: A realistic Continual Learning benchmark for Autonomous Driving	Oct 7, 2022	Autonomous DrivingContinual Learning	CodeCode Available	1	5
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models	Mar 28, 2024	Continual Learning	CodeCode Available	1	5
Class Incremental Learning via Likelihood Ratio Based Task Prediction	Sep 26, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning	May 30, 2025	class-incremental learningClass Incremental 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
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning	Jan 3, 2020	Continual Learningimage-classification	CodeCode Available	1	5
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding	Dec 13, 2022	Continual LearningIncremental Learning	CodeCode Available	1	5
A Neural Span-Based Continual Named Entity Recognition Model	Feb 23, 2023	Continual LearningContinual Named Entity Recognition	CodeCode Available	1	5
CaT: Balanced Continual Graph Learning with Graph Condensation	Sep 18, 2023	Continual LearningGraph Learning	CodeCode Available	1	5
CBA: Improving Online Continual Learning via Continual Bias Adaptor	Aug 14, 2023	Continual Learning	CodeCode Available	1	5
An Empirical Investigation of the Role of Pre-training in Lifelong Learning	Dec 16, 2021	Continual Learningimage-classification	CodeCode Available	1	5
A Comprehensive Survey of Continual Learning: Theory, Method and Application	Jan 31, 2023	Continual LearningLearning Theory	CodeCode Available	1	5
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning	Aug 18, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification	Mar 22, 2021	Continual LearningGeneral Classification	CodeCode Available	1	5
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation	Mar 13, 2023	Continual LearningExemplar-Free	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
A Theoretical Study on Solving Continual Learning	Nov 4, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning	Mar 31, 2024	Continual LearningRepresentation Learning	CodeCode Available	1	5
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials	May 11, 2022	Continual LearningDeepFake Detection	CodeCode Available	1	5
CCL: Continual Contrastive Learning for LiDAR Place Recognition	Mar 24, 2023	Autonomous DrivingContinual Learning	CodeCode Available	1	5
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTS	Apr 5, 2021	Continual LearningMulti-agent Reinforcement 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