Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Deep Residual Learning for Image Recognition

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Adam: A Method for Stochastic Optimization

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Learning Multiple Layers of Features from Tiny Images

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On Information and Sufficiency

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Distilling the Knowledge in a Neural Network

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Overcoming catastrophic forgetting in neural networks

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Deep Residual Learning for Image Recognition

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iCaRL: Incremental Classifier and Representation Learning

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Natural Gradient Works Efficiently in Learning

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Generative Feature Replay with Orthogonal Weight Modification for Continual Learning

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Multi-Domain Multi-Task Rehearsal for Lifelong Learning

Lyu, F., Wang, S., Feng, W., Ye, Z., Hu, F., & Wang, S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17068 · Cited by 21

Class incremental learning for wildlife biodiversity monitoring in camera trap images

Zhu, H., Tian, Y., & Zhang, J. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101760 · Cited by 22

Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data

Kurmanji, M., & Triantafillou, P. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588713 · Cited by 21

Online continual learning for human activity recognition

Schiemer, M., Fang, L., Dobson, S., & Ye, J. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101817 · Cited by 23

CBA: Improving Online Continual Learning via Continual Bias Adaptor

Wang, Q., Wang, R., Wu, Y., Jia, X., & Meng, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01749 · Cited by 24

Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery

Xu, M., Islam, M., Bai, L., & Ren, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3364969 · Cited by 22

LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery

Chen, K., Du, Y., You, T., Islam, M., Guo, Z., Jin, Y., Chen, G., & Heng, P. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610603 · Cited by 21

Tf-GCZSL: Task-free generalized continual zero-shot learning

Gautam, C., Parameswaran, S., Mishra, A., & Sundaram, S. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.08.034 · Cited by 21

Continual Hippocampus Segmentation with Transformers

Ranem, A., González, C., & Mukhopadhyay, A. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00415 · Cited by 22

Continual learning for online state of charge estimation across diverse lithium-ion batteries

Yao, J., Zheng, B., & Kowal, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116086 · Cited by 20

Continual learning with attentive recurrent neural networks for temporal data classification

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Few-Shot Class-Incremental Learning for 3D Point Cloud Objects

Chowdhury, T. F., Cheraghian, A., Ramasinghe, S., Ahmadi, S., Saberi, M., & Rahman, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_12 · Cited by 20

Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer

Marsden, R. A., Wiewel, F., Döbler, M., Yang, Y., & Yang, B. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892200 · Cited by 20

Bilateral Memory Consolidation for Continual Learning

Nie, X., Shi-xiong, X., Liu, X., Meng, G., Huo, C., & Xiang, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01538 · Cited by 20

Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation

Yu, C., Zhou, Q., Li, J., Yuan, J., Wang, Z., & Wang, F. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02268 · Cited by 22

Continual Nuclei Segmentation via Prototype-Wise Relation Distillation and Contrastive Learning

Wu, H., Wang, Z., Zhao, Z., Chen, C., & Qin, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3307892 · Cited by 20

Evolving Parameterized Prompt Memory for Continual Learning

Kurniawan, M. R., Song, X. R., Ma, Z., He, Y., Gong, Y., Qi, Y., & Wei, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29231 · Cited by 20

MIND: Multi-Task Incremental Network Distillation

Bonato, J., Pelosin, F., Sabetta, L., & Nicolosi, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28987 · Cited by 22

Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations

Kolouri, S., Ketz, N., Soltoggio, A., & Pilly, P. K. (2020) · Loughborough University Research Repository (Loughborough University) · Cited by 20

Unsupervised Continual Learning for Gradually Varying Domains

Taufique, A. M. N., Jahan, C. S., & Savakis, A. E. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00418 · Cited by 21

Ternary Feature Masks: continual learning without any forgettingPreprint

Masana, M., Tuytelaars, T., & van de Weijer, J. (2020) · arXiv (Cornell University) · Cited by 20

Graph-Based Continual LearningPreprint

Tang, B., & Matteson, D. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04813 · Cited by 25

Topology-aware Embedding Memory for Continual Learning on Expanding Networks

Zhang, X., Song, D., Chen, Y., & Tao, D. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671732 · Cited by 19

AirLoop: Lifelong Loop Closure Detection

Gao, D., Wang, C., & Scherer, S. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811658 · Cited by 19

Differential Privacy Preservation in Robust Continual Learning

Hassanpour, A., Moradikia, M., Yang, B., Abdelhadi, A., Busch, C., & Fiérrez, J. (2022) · IEEE Access · doi:10.1109/access.2022.3154826 · Cited by 23

Hierarchically structured task-agnostic continual learning

Hihn, H., & Braun, D. A. (2022) · Machine Learning · doi:10.1007/s10994-022-06283-9 · Cited by 20

Improving Replay Sample Selection and Storage for Less Forgetting in Continual Learning

Brignac, D., Lobo, N. D. V., & Mahalanobis, A. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00380 · Cited by 19

Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification

Cui, Z., Zhou, J., Wang, X., Zhu, M., & Peng, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01572 · Cited by 19

Real-world Cross-modal Retrieval via Sequential Learning

Song, G., & Tan, X. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3002177 · Cited by 19

Static-Dynamic Co-teaching for Class-Incremental 3D Object Detection

Zhao, N., & Lee, G. H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20254 · Cited by 19

Online Class-Incremental Learning For Real-World Food Image Classification

Raghavan, S., He, J., & Zhu, F. M. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00801 · Cited by 19

Meta-Learning with Sparse Experience Replay for Lifelong Language LearningPreprint

Holla, N., Mishra, P., Yannakoudakis, H., & Shutova, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.04891 · Cited by 24

Data Augmented Flatness-aware Gradient Projection for Continual Learning

Yang, E., Shen, L., Wang, Z., Liu, S., Guo, G., & Wang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00518 · Cited by 18

Adaptive Mixture-of-Experts Distillation for Cross-Satellite Generalizable Incremental Remote Sensing Scene Classification

Fu, Y., Yang, R., Liu, Z., & Ng, M. K. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3598274 · Cited by 19

NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning

Malepathirana, T., Senanayake, D., & Halgamuge, S. K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01072 · Cited by 19

Improving CLIP Fine-tuning Performance

Wei, Y., Hu, H., Xie, Z., Liu, Z., Zhang, Z., Cao, Y., Bao, J., Chen, D., & Guo, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00501 · Cited by 20

Class-incremental learning with causal relational replay

Nguyen, T. Q., Kieu, D., Duong, B., Kieu, T., Do, K., Nguyen, T., & Le, B. H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123901 · Cited by 18

CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data

Bringas, S., Duque, R., Lage, C., & Montaña, J. L. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3392354 · Cited by 18

Same State, Different Task: Continual Reinforcement Learning without Interference

Kessler, S., Parker-Holder, J., Ball, P., Zohren, S., & Roberts, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20674 · Cited by 23

New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning

Liang, G., Chen, Z., Chen, Z., Ji, S., & Zhang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3325651 · Cited by 20

Advances in continual learning: A comprehensive review

Jiang, M., Fan, J., & Li, F. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128739 · Cited by 18

Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning

Zhou, D., Cai, Z.-W., Ye, H.-J., Zhang, L., & Zhan, D. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01913 · Cited by 24

Learning at a Glance: Towards Interpretable Data-Limited Continual Semantic Segmentation via Semantic-Invariance Modelling

Yuan, B., Zhao, D., & Shi, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3396809 · Cited by 17

Overcoming Catastrophic Forgetting Using Sparse Coding and Meta Learning

Hurtado, J., Löbel, H., & Soto, Á. (2021) · IEEE Access · doi:10.1109/access.2021.3090672 · Cited by 17

CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsPreprint

Powers, S., Xing, E., Kolve, E., Mottaghi, R., & Gupta, A. (2021) · CoLLAs · doi:10.48550/arxiv.2110.10067 · Cited by 52

A soft nearest-neighbor framework for continual semi-supervised learning

Kang, Z., Fini, E., Nabi, M., Ricci, E., & Alahari, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01090 · Cited by 16

LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

Oveis, A. H., Giusti, E., Ghio, S., Meucci, G., & Martorella, M. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3318675 · Cited by 16

Large-scale continual learning for ancient Chinese character recognition

Xu, Y., Zhang, X.-Y., Zhang, Z., & Liu, C. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110283 · Cited by 16

Class-Incremental Recognition of Objects in Remote Sensing Images With Dynamic Hybrid Exemplar Selection

Fu, Y., Liu, Z., Wu, C., Wu, F., & Liu, M. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3363114 · Cited by 16

MoBoo: Memory-Boosted Vision Transformer for Class-Incremental Learning

Ni, B., Nie, X., Zhang, C., Xu, S., Zhang, X., Meng, G., & Xiang, S. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3417431 · Cited by 16

MUFTI: Multi-Domain Distillation-Based Heterogeneous Federated Continuous Learning

Gai, K., Wang, Z., Yu, J., & Zhu, L. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3542246 · Cited by 17

Online-LoRA: Task-Free Online Continual Learning via Low Rank Adaptation

Wei, X., Li, G., & Marculescu, R. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00646 · Cited by 16

An Open-Set Few-Shot Class Incremental Learning Framework for Specific Emitter Identification

Zhang, T., Shen, X., Shang, Z., Ding, G., Wu, H., & Qiao, X. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3565589 · Cited by 16

Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications

Minhas, M. F., Putra, R. V. W., Awwad, F. R., Hasan, O., & Shafique, M. (2025) · IEEE Access · doi:10.1109/access.2025.3588665 · Cited by 24

Continual learning approaches for anomaly detection

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Drinking from a Firehose: Continual Learning with Web-scale Natural Language

Hu, H., Şener, O., Sha, F., & Koltun, V. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218265 · Cited by 23

Selective Replay Enhances Learning in Online Continual Analogical Reasoning

Hayes, T. L., & Kanan, C. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00389 · Cited by 16

GopGAN: Gradients Orthogonal Projection Generative Adversarial Network With Continual Learning

Li, X., & Wang, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3093319 · Cited by 16

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

Cermelli, F., Mancini, M., Bulo, S. R., Ricci, E., & Barbara Caputo, B. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3133954 · Cited by 16

AGCN: Augmented Graph Convolutional Network for Lifelong Multi-Label Image Recognition

Du, K., Lyu, F., Hu, F., Li, L., Feng, W., Xu, F., & Fu, Q. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859622 · Cited by 16

Continual prune-and-select: class-incremental learning with specialized subnetworks

Dekhovich, A., Tax, D. M. J., Sluiter, M. H. F., & Bessa, M. A. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04441-z · Cited by 20

Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and IntransigencePreprint

Kim, D., Bae, J.-H., Jo, Y., & Choi, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00829 · Cited by 23

CGoFed: Constrained Gradient Optimization Strategy for Federated Class Incremental Learning

Feng, J., Yang, X., Liang, L., Han, W., Fang, B., & Liao, Q. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3544605 · Cited by 15

MiCro: Modeling Cross-Image Semantic Relationship Dependencies for Class-Incremental Semantic Segmentation in Remote Sensing Images

Rong, X., Wang, P., Diao, W., Yang, Y., Yin, W., Zeng, X., Wang, H., & Sun, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3297203 · Cited by 15

Preserving Linear Separability in Continual Learning by Backward Feature Projection

Gu, Q., Shim, D., & Shkurti, F. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02326 · Cited by 15

Modal adaptive super-resolution for medical images via continual learning

Wu, Z., Zhu, F., Guo, K., Ren, S., Chao, L., & Fang, H. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109342 · Cited by 15

Relationship-Guided Knowledge Transfer for Class-Incremental Facial Expression Recognition

Lv, Y., Yan, Y., Xue, J., Chen, S., & Wang, H. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3374116 · Cited by 15

A sparse quantized hopfield network for online-continual memory

Alonso, N., & Krichmar, J. L. (2024) · Nature Communications · doi:10.1038/s41467-024-46976-4 · Cited by 17

Exemplar-Free Lifelong Person Re-identification via Prompt-Guided Adaptive Knowledge Consolidation

Li, Q., Xu, K., Peng, Y., & Zhou, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02110-x · Cited by 16

Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning

Qiu, Z., Xu, Y. T., Meng, F., Li, H., Xu, L., & Wu, Q. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02268 · Cited by 15

Enhancing Visual Continual Learning with Language-Guided Supervision

Ni, B., Zhao, H., Zhang, C., Hu, K., Meng, G., Zhang, Z., & Xiang, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02272 · Cited by 17

Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation

Xie, Z., Lu, H., Xiao, J.-W., Wang, E., Zhang, L., & Liu, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_11 · Cited by 15

Mitigating Background Shift in Class-Incremental Semantic Segmentation

Park, G., Moon, W., Lee, S., Kim, T.-Y., & Heo, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72973-7_5 · Cited by 17

Mitigating Catastrophic Forgetting in Cross-Domain Fault Diagnosis: An Unsupervised Class Incremental Learning Network Approach

Zhan, Y., Yang, R., Zhang, Y., & Wang, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3500047 · Cited by 15

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Pietras, B., Schmutz, V., & Schwalger, T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010809 · Cited by 16

Incremental Task Learning with Incremental Rank Updates

Hyder, R., Shao, K., Hou, B., Markopoulos, P. P., Prater-Bennette, A., & Asif, M. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20050-2_33 · Cited by 15

A multi-label Continual Learning framework to scale deep learning approaches for packaging equipment monitoring

Pezze, D. D., Deronjic, D., Masiero, C., Tosato, D., Beghi, A., & Susto, G. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106610 · Cited by 15

Single-Net Continual Learning with Progressive Segmented Training

Du, X., Charan, G., Liu, F., & Cao, Y. K. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00267 · Cited by 15

Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning

Kessler, S., Nguyen, V., Zohren, S., & Roberts, S. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1912.02290 · Cited by 15

Tackling Catastrophic Forgetting and Background Shift in Continual Semantic SegmentationPreprint

Douillard, A., Chen, Y., Dapogny, A., & Cord, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15287 · Cited by 18

CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and ComparabilityPreprint

Mundt, M., Lang, S., Delfosse, Q., & Kersting, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03331 · Cited by 16

Carousel Memory: Rethinking the Design of Episodic Memory for Continual LearningPreprint

Lee, S., Weerakoon, M., Choi, J., Zhang, M., Wang, D., & Jeon, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07276 · Cited by 15

Visual-Textual Attribute Learning for Class-Incremental Facial Expression Recognition

Lv, Y., Huang, G., Yan, Y., Xue, J., Chen, S., & Wang, H. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3374573 · Cited by 14

Adaptive federated class-Incremental learning for reducing catastrophic forgetting

You, Z., Chu, J., Li, Z., 刘, 斌., & Li, T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128442 · Cited by 14

A Survey of Multi-Agent Reinforcement Learning for Cooperative Control in Multi-AUV Systems

Wibisono, A., Song, H.-K., & Lee, B. M. (2025) · IEEE Access · doi:10.1109/access.2025.3609457 · Cited by 14

Online continual learning via the knowledge invariant and spread-out properties

Han, Y.-N., & Liu, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119004 · Cited by 14

Cooperative data-driven modeling

Dekhovich, A., Turan, O. T., Yi, J., & Bessa, M. A. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116432 · Cited by 17

Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

Li, J., Azizov, D., Li, Y., & Liang, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29259 · Cited by 14

CoLLaRS : A cloud–edge–terminal collaborative lifelong learning framework for AIoT

Hu, S., Lin, J., Lu, Z., Du, X., Duan, Q., & Huang, S.-C. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.046 · Cited by 14

Replay Without Saving: Prototype Derivation and Distribution Rebalance for Class-Incremental Semantic Segmentation

Chen, J., Cong, R., Luo, Y., Ip, H. H. S., & Kwong, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545966 · Cited by 14

ISM-Net: Mining incremental semantics for class incremental learning

Qiu, Z., Xu, L., Wang, Z., Wu, Q., Meng, F., & Li, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.12.029 · Cited by 14

FoCL: Feature-oriented continual learning for generative models

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Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay ProcessesPreprint

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