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 401–450 of 2644 papers

Title	Date	Tasks	Status	Hype	Score
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning	Mar 8, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning	Aug 18, 2023	class-incremental learningClass Incremental 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
Coresets via Bilevel Optimization for Continual Learning and Streaming	Jun 6, 2020	Bilevel OptimizationContinual Learning	CodeCode Available	1	5
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal	Feb 2, 2023	Continual Learning	CodeCode Available	1	5
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research	Nov 15, 2022	Continual LearningDiversity	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
CPR: Classifier-Projection Regularization for Continual Learning	Jun 12, 2020	Continual Learning	CodeCode Available	1	5
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments	Dec 31, 2021	Continual LearningDeep 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
Class Incremental Learning via Likelihood Ratio Based Task Prediction	Sep 26, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Class-Incremental Learning with Generative Classifiers	Apr 20, 2021	class-incremental learningClass Incremental Learning	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
Online Continual Learning for Embedded Devices	Mar 21, 2022	Continual 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
CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation	Aug 14, 2023	Continual LearningContinual Pretraining	CodeCode Available	1	5
A soft nearest-neighbor framework for continual semi-supervised learning	Dec 9, 2022	Continual Learning	CodeCode Available	1	5
CTP:Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation	Jan 1, 2023	Continual LearningContinual Pretraining	CodeCode Available	1	5
Essentials for Class Incremental Learning	Feb 18, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Dark Experience for General Continual Learning: a Strong, Simple Baseline	Apr 15, 2020	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
DAM: Dynamic Adapter Merging for Continual Video QA Learning	Mar 13, 2024	Continual Learningimage-classification	CodeCode Available	1	5
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning	Mar 12, 2020	Continual LearningMeta-Learning	CodeCode Available	1	5
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING	Dec 7, 2020	Computational EfficiencyContinual Learning	CodeCode Available	1	5
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster Memory	Jan 1, 2024	Continual LearningDiversity	CodeCode Available	1	5
Dataset Condensation with Contrastive Signals	Feb 7, 2022	AttributeContinual 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
Dataset Condensation with Gradient Matching	Jun 10, 2020	Continual LearningDataset Condensation	CodeCode Available	1	5
On the Limitations of Continual Learning for Malware Classification	Aug 13, 2022	Continual LearningGeneral Classification	CodeCode Available	1	5
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks	Jun 18, 2022	Continual LearningTransfer Learning	CodeCode Available	1	5
Optimizing Reusable Knowledge for Continual Learning via Metalearning	Jun 9, 2021	Continual Learning	CodeCode Available	1	5
Avalanche RL: a Continual Reinforcement Learning Library	Feb 28, 2022	Continual LearningOpenAI Gym	CodeCode Available	1	5
Action Flow Matching for Continual Robot Learning	Apr 25, 2025	Continual 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
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
Defining Benchmarks for Continual Few-Shot Learning	Apr 15, 2020	continual few-shot learningContinual Learning	CodeCode Available	1	5
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning	Nov 15, 2017	Continual LearningNetwork Pruning	CodeCode Available	1	5
CLoG: Benchmarking Continual Learning of Image Generation Models	Jun 7, 2024	BenchmarkingContinual Learning	CodeCode Available	1	5
Patch-based Knowledge Distillation for Lifelong Person Re-Identification	Oct 10, 2022	Continual LearningKnowledge Distillation	CodeCode Available	1	5
Co^2L: Contrastive Continual Learning	Jun 28, 2021	Continual LearningContrastive Learning	CodeCode Available	1	5
Avalanche: an End-to-End Library for Continual Learning	Apr 1, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
Personalized Federated Continual Learning via Multi-granularity Prompt	Jun 27, 2024	Continual LearningFederated Learning	CodeCode Available	1	5
Co2L: Contrastive Continual Learning	Jan 1, 2021	Continual LearningContrastive Learning	CodeCode Available	1	5
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues	Sep 18, 2023	Continual LearningContrastive Learning	CodeCode Available	1	5
EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video	Jul 11, 2023	Action RecognitionContinual Learning	CodeCode Available	1	5
CLR: Channel-wise Lightweight Reprogramming for Continual Learning	Jul 21, 2023	Continual Learningimage-classification	CodeCode Available	1	5
Predictive Experience Replay for Continual Visual Control and Forecasting	Mar 12, 2023	Continual LearningModel-based Reinforcement Learning	CodeCode Available	1	5
PrePrompt: Predictive prompting for class incremental learning	May 13, 2025	Classifier calibrationclass-incremental learning	CodeCode Available	1	5
Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition	Oct 8, 2022	Causal InferenceContinual Learning	CodeCode Available	1	5
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding	Sep 15, 2023	Continual LearningObject	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	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