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
PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning	Mar 10, 2025	class-incremental learningClass Incremental Learning	—Unverified	0
A Good Start Matters: Enhancing Continual Learning with Data-Driven Weight Initialization	Mar 9, 2025	Continual Learning	—Unverified	0
Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs	Mar 8, 2025	Continual Learning	—Unverified	0
Minion Gated Recurrent Unit for Continual Learning	Mar 8, 2025	Continual Learningimage-classification	—Unverified	0
Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification	Mar 8, 2025	Continual LearningDiagnostic	—Unverified	0
Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection	Mar 6, 2025	class-incremental learningClass Incremental Learning	—Unverified	0
Knowledge Retention for Continual Model-Based Reinforcement Learning	Mar 6, 2025	Continual LearningModel-based Reinforcement Learning	—Unverified	0
No Forgetting Learning: Memory-free Continual Learning	Mar 6, 2025	Continual LearningKnowledge Distillation	—Unverified	0
CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models	Mar 6, 2025	BenchmarkingContinual Learning	CodeCode Available	0
Synthetic Data is an Elegant GIFT for Continual Vision-Language Models	Mar 6, 2025	Continual LearningImage Generation	—Unverified	0
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection	Mar 4, 2025	Anomaly DetectionContinual Learning	—Unverified	0
Parabolic Continual Learning	Mar 3, 2025	Continual Learning	—Unverified	0
Alchemist: Towards the Design of Efficient Online Continual Learning System	Mar 3, 2025	Code CompletionContinual Learning	—Unverified	0
Advancing Prompt-Based Methods for Replay-Independent General Continual Learning	Mar 2, 2025	Continual Learning	CodeCode Available	1
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering	Mar 1, 2025	Continual LearningLanguage Modeling	—Unverified	0
Efficient Prompting for Continual Adaptation to Missing Modalities	Mar 1, 2025	Continual Learning	—Unverified	0
Continual Learning-Aided Super-Resolution Scheme for Channel Reconstruction and Generalization in OFDM Systems	Feb 28, 2025	Continual LearningSuper-Resolution	—Unverified	0
One-for-More: Continual Diffusion Model for Anomaly Detection	Feb 27, 2025	Anomaly Detectioncontinual anomaly detection	CodeCode Available	2
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge Transfer	Feb 27, 2025	Continual LearningTransfer Learning	CodeCode Available	0
PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems	Feb 26, 2025	Continual LearningMulti-Task Learning	—Unverified	0
PVBF: A Framework for Mitigating Parameter Variation Imbalance in Online Continual Learning	Feb 25, 2025	Continual Learning	—Unverified	0
On-device edge learning for IoT data streams: a survey	Feb 25, 2025	Continual LearningMeta-Learning	—Unverified	0
SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models	Feb 25, 2025	Continual LearningGSM8K	—Unverified	0
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Models	Feb 25, 2025	Continual Learning	—Unverified	0
CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation	Feb 24, 2025	3D Instance SegmentationContinual Learning	CodeCode Available	0
Efficient 4D Gaussian Stream with Low Rank Adaptation	Feb 23, 2025	Continual LearningNovel View Synthesis	—Unverified	0
Few-shot Continual Relation Extraction via Open Information Extraction	Feb 23, 2025	Continual LearningContinual Relation Extraction	—Unverified	0
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning	Feb 22, 2025	Continual LearningLanguage Modeling	—Unverified	0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning	Feb 22, 2025	ARCContinual Learning	—Unverified	0
On the Design of Safe Continual RL Methods for Control of Nonlinear Systems	Feb 21, 2025	Continual LearningMuJoCo	CodeCode Available	0
MoMa: A Modular Deep Learning Framework for Material Property Prediction	Feb 21, 2025	Continual LearningDeep Learning	—Unverified	0
Online hand gesture recognition using Continual Graph Transformers	Feb 20, 2025	Action RecognitionContinual Learning	—Unverified	0
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models	Feb 20, 2025	Continual LearningKnowledge Graphs	CodeCode Available	7
Accurate Forgetting for Heterogeneous Federated Continual Learning	Feb 20, 2025	Continual LearningFederated Learning	CodeCode Available	0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems	Feb 19, 2025	Continual LearningIntrusion Detection	—Unverified	0
A Survey of Text Classification Under Class Distribution Shift	Feb 18, 2025	ArticlesContinual Learning	CodeCode Available	0
Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks	Feb 18, 2025	Continual LearningISAC	—Unverified	0
On the Computation of the Fisher Information in Continual Learning	Feb 17, 2025	Continual Learning	CodeCode Available	5
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent	Feb 17, 2025	Continual Learningparameter-efficient fine-tuning	—Unverified	0
Exploiting Task Relationships for Continual Learning Using Transferability-Aware Task Embeddings	Feb 17, 2025	Continual LearningPermuted-MNIST	—Unverified	0
Continual Learning Should Move Beyond Incremental Classification	Feb 17, 2025	ClassificationContinual Learning	—Unverified	0
DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free Continual Learning	Feb 17, 2025	Continual Learning	CodeCode Available	0
Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning	Feb 16, 2025	Continual Learning	—Unverified	0
Neural Networks Remember More: The Power of Parameter Isolation and Combination	Feb 16, 2025	Continual LearningTask Arithmetic	—Unverified	0
Non-Uniform Memory Sampling in Experience Replay	Feb 16, 2025	Continual Learning	CodeCode Available	0
ReReLRP - Remembering and Recognizing Tasks with LRP	Feb 15, 2025	Continual Learning	CodeCode Available	0
Ten Challenging Problems in Federated Foundation Models	Feb 14, 2025	Continual LearningFederated Learning	—Unverified	0
Feature-based Graph Attention Networks Improve Online Continual Learning	Feb 13, 2025	Continual LearningGraph Attention	—Unverified	0
Replay-free Online Continual Learning with Self-Supervised MultiPatches	Feb 13, 2025	Continual LearningSelf-Supervised Learning	CodeCode Available	0
Vertical Federated Continual Learning via Evolving Prototype Knowledge	Feb 13, 2025	Continual LearningFederated Learning	—Unverified	0

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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