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 1301–1350 of 2644 papers

Title	Date	Tasks	Status	Hype
Accelerating Batch Active Learning Using Continual Learning Techniques	May 10, 2023	Active LearningContinual Learning	—Unverified	0
Continual Facial Expression Recognition: A Benchmark	May 10, 2023	Continual LearningFacial Expression Recognition	—Unverified	0
Investigating Forgetting in Pre-Trained Representations Through Continual Learning	May 10, 2023	Continual LearningGeneral Knowledge	—Unverified	0
DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors	May 9, 2023	Continual LearningSynthetic Data Generation	—Unverified	0
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning	May 8, 2023	Continual LearningDiversity	CodeCode Available	1
Multi-agent Continual Coordination via Progressive Task Contextualization	May 7, 2023	Continual LearningMulti-agent Reinforcement Learning	—Unverified	0
Active Continual Learning: On Balancing Knowledge Retention and Learnability	May 6, 2023	Active LearningContinual Learning	—Unverified	0
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code	May 6, 2023	Continual LearningGeneral Knowledge	—Unverified	0
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning	May 5, 2023	Continual LearningSelf-Supervised Learning	—Unverified	0
Evolving Dictionary Representation for Few-shot Class-incremental Learning	May 3, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning	May 3, 2023	Continual LearningRelational Reasoning	—Unverified	0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions	May 3, 2023	Continual LearningDataset Distillation	—Unverified	0
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning	May 1, 2023	ClusteringContinual Learning	CodeCode Available	0
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning	Apr 30, 2023	Continual Learning	—Unverified	0
The Ideal Continual Learner: An Agent That Never Forgets	Apr 29, 2023	Continual LearningGeneralization Bounds	CodeCode Available	4
Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection	Apr 27, 2023	3D Object DetectionAll	CodeCode Available	0
Adversary Aware Continual Learning	Apr 27, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Learning to Predict Navigational Patterns from Partial Observations	Apr 26, 2023	Continual LearningData Augmentation	CodeCode Available	1
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR	Apr 26, 2023	Activity RecognitionContinual Learning	—Unverified	0
Towards Compute-Optimal Transfer Learning	Apr 25, 2023	Computational EfficiencyContinual Learning	—Unverified	0
Renate: A Library for Real-World Continual Learning	Apr 24, 2023	Continual Learning	CodeCode Available	2
SequeL: A Continual Learning Library in PyTorch and JAX	Apr 21, 2023	Continual Learning	CodeCode Available	1
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning	Apr 20, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
A baseline on continual learning methods for video action recognition	Apr 20, 2023	Action RecognitionContinual Learning	—Unverified	0
Robust Deep Reinforcement Learning Scheduling via Weight Anchoring	Apr 20, 2023	Continual LearningDeep Reinforcement Learning	CodeCode Available	0
Regularizing Second-Order Influences for Continual Learning	Apr 20, 2023	Continual Learning	CodeCode Available	1
trust-Region Adaptive Frequency for Online Continual Learning	Apr 18, 2023	Continual Learning	—Unverified	0
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases	Apr 18, 2023	Continual Learning	CodeCode Available	1
Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation	Apr 15, 2023	Continual LearningDomain Adaptation	CodeCode Available	1
Continual Source-Free Unsupervised Domain Adaptation	Apr 14, 2023	Continual LearningDomain Adaptation	—Unverified	0
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition	Apr 14, 2023	Action RecognitionContinual Learning	—Unverified	0
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning	Apr 13, 2023	Continual Learning	CodeCode Available	0
Taxonomic Class Incremental Learning	Apr 12, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA	Apr 12, 2023	Continual Learningimage-classification	—Unverified	0
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning	Apr 10, 2023	Continual LearningDiagnostic	—Unverified	0
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning	Apr 10, 2023	Continual Learning	CodeCode Available	1
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning	Apr 9, 2023	Continual LearningSemantic Segmentation	CodeCode Available	0
Does Continual Learning Equally Forget All Parameters?	Apr 9, 2023	AllAttribute	—Unverified	0
MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning	Apr 8, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data	Apr 8, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1
Exploring Data Geometry for Continual Learning	Apr 8, 2023	Continual Learning	—Unverified	0
A multifidelity approach to continual learning for physical systems	Apr 8, 2023	Continual Learning	CodeCode Available	1
Asynchronous Federated Continual Learning	Apr 7, 2023	Continual LearningFederated Learning	CodeCode Available	1
I2I: Initializing Adapters with Improvised Knowledge	Apr 4, 2023	Continual LearningQuestion Answering	CodeCode Available	1
Online Distillation with Continual Learning for Cyclic Domain Shifts	Apr 3, 2023	Autonomous DrivingContinual Learning	CodeCode Available	0
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting	Apr 3, 2023	Continual LearningImage Classification	CodeCode Available	0
Towards Adversarially Robust Continual Learning	Mar 31, 2023	Adversarial RobustnessContinual Learning	—Unverified	0
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning	Mar 30, 2023	Continual LearningKnowledge Distillation	CodeCode Available	1
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling	Mar 30, 2023	Continual LearningData Poisoning	CodeCode Available	0
How Efficient Are Today's Continual Learning Algorithms?	Mar 29, 2023	Continual 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