The DOI matches its Crossref record; 29 of the 33 references it cites were found in a registry or an index.
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Rwalk improves the regularization methods of EWC and SI, and further improves the performance based on them. However, for performance reasons, some old data are still retained. This shows the effectiveness of rehearsal strategies for mitigating catastrophic forgetting.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [47]
In the citing paper
“Many incremental learning methods based on the regularization strategy save a small amount of old data to enhance model performance [47,70,76].”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [47]
In the citing paper
“The experimental results of [47,70,76] prove that the algorithm based on the regularization strategy is appropriately combined with a rehearsal or pseudo-rehearsal strategy, which is of great help to improve the performance of the model.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [47]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988
Natural Gradient Works Efficiently in Learning
Амари, Ш.-И. (1998) · Neural Computation · doi:10.1162/089976698300017746 · Cited by 2,914
Theory of the backpropagation neural network
Hecht-Nielsen (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118638 · Cited by 2,274
Research works printed but not linked: 1
- Le Roux, N., Pierre-Antoine, M., Bengio, Y.: Topmoumoute online natural gradient algorithm. In: NIPS (2007)
Cited by
Sorted by citations
Items 101–200 of 1,000
Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces
Cheraghian, A., Rahman, S., Ramasinghe, S., Fang, P., Simon, C., Petersson, L., & Harandi, M. T. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00854 · Cited by 69
Model Behavior Preserving for Class-Incremental Learning
Liu, Y., Hong, X., Tao, X., Dong, S., Shi, J., & Gong, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3144183 · Cited by 71
Layerwise Optimization by Gradient Decomposition for Continual Learning
Tang, S., Chen, D., Zhu, J., Yu, S., & Ouyang, W. (2021) · 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/cvpr46437.2021.00951 · Cited by 68
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector
Lin, G., Chu, H., & Lai, H. (2022) · 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/cvpr52688.2022.00019 · Cited by 67
Graph Lifelong Learning: A Survey
Febrinanto, F. G., Xia, F., Moore, K., Thapa, C., & Aggarwal, C. C. (2023) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2022.3222049 · Cited by 73
Memorizing Complementation Network for Few-Shot Class-Incremental Learning
Ji, Z., Hou, Z., Liu, X., Pang, Y., & Li, X. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3236160 · Cited by 67
Online Prototype Learning for Online Continual Learning
Wei, Y., Ye, J., Huang, Z., Zhang, J., & Shan, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01720 · Cited by 67
Efficient Feature Transformations for Discriminative and Generative Continual Learning
Verma, V. K., Liang, K. J., Mehta, N., Rai, P., & Carin, L. (2021) · 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/cvpr46437.2021.01365 · Cited by 71
Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation
Phan, V. M. H., Ta, T.-A., Phung, S. L., Tran-Thanh, L., & Bouzerdoum, A. (2022) · 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/cvpr52688.2022.01636 · Cited by 66
Incremental Learning in Semantic Segmentation from Image Labels
Cermelli, F., Fontanel, D., Tavera, A., Ciccone, M., & Barbara Caputo, B. (2022) · 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/cvpr52688.2022.00433 · Cited by 67
Continual learning in sensor-based human activity recognition: An empirical benchmark analysis
Jha, S., Schiemer, M., Zambonelli, F., & Ye, J. (2021) · Information Sciences · doi:10.1016/j.ins.2021.04.062 · Cited by 63
Continuous transfer of neural network representational similarity for incremental learning
Tian, S., Li, W., Ning, X., Ran, H., Qin, H., & Tiwari, P. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126300 · Cited by 65
Continual Learning With Knowledge Distillation: A Survey
Li, S., Su, T., Zhang, X.-Y., & Wang, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476068 · Cited by 71
Generating Instance-level Prompts for Rehearsal-free Continual Learning
Jung, D., Han, D., Bang, J., & Song, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01088 · Cited by 69
Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition
Dang, S., Cao, Z., Cui, Z., Pi, Y., & Liu, N. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2970076 · Cited by 61
Huang, Z., Zhang, Z., Lan, C., Zeng, W., Chu, P., You, Q., Wang, J., Liu, Z., & Zha, Z.-J. (2022) · 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/cvpr52688.2022.01389 · Cited by 59
Continual Learning with Adaptive Weights (CLAW)
Adel, T., Zhao, H., & Turner, R. E. (2019) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · doi:10.48550/arxiv.1911.09514 · Cited by 66
Multi-Domain Incremental Learning for Semantic Segmentation
Garg, P., Saluja, R., Balasubramanian, V. N., Arora, C., Subramanian, A., & Jawahar, C. V. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00214 · Cited by 58
Recent Advances of Foundation Language Models-based Continual Learning: A Survey
Yang, Y., Zhou, J., Ding, X., Huai, T., Liu, S., Chen, Q., Xie, Y., & He, L. (2024) · ACM Computing Surveys · doi:10.1145/3705725 · Cited by 63
Online Continual Learning Under Extreme Memory Constraints
Fini, E., Lathuilière, S., Sangineto, E., Nabi, M., & Ricci, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_43 · Cited by 57
A Unifying Bayesian View of Continual LearningPreprint
Farquhar, S., & Gal, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06494 · Cited by 80
Striking a Balance between Stability and Plasticity for Class-Incremental Learning
Wu, G., Gong, S., & Queen, P. L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00116 · Cited by 56
Edge Continual Learning for Dynamic Digital Twins over Wireless Networks
Hashash, O., Chaccour, C., & Saad, W. (2022) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc51304.2022.9833928 · Cited by 56
Continual Learning on Dynamic Graphs via Parameter Isolation
Zhang, P., Yan, Y., Li, C., Wang, S., Xie, X., Song, G., & Kim, S. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591652 · Cited by 66
Online Learned Continual Compression with Adaptive Quantization ModulesPreprint
Caccia, L., Belilovsky, E., Caccia, M., & Pineau, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08019 · Cited by 55
Linear Mode Connectivity in Multitask and Continual LearningPreprint
Mirzadeh, S. I., Farajtabar, M., Görür, D., Pascanu, R., & Ghasemzadeh, H. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04495 · Cited by 55
Continual Learning in Low-rank Orthogonal Subspaces
Chaudhry, A., Khan, N., Dokania, P. K., & Torr, P. H. S. (2020) · arXiv · doi:10.48550/arxiv.2010.11635 · Cited by 54
Self-citationYu, C., Zhao, X., Gong, B., Hu, Y., Song, M., Yu, H., & Chang, C. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3359629 · Cited by 54
Preserving and combining knowledge in robotic lifelong reinforcement learning
Meng, Y., Bing, Z., Yao, X., Chen, K., Huang, K., Gao, Y., Sun, F., & Knoll, A. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00983-2 · Cited by 59
Triple-Memory Networks: A Brain-Inspired Method for Continual Learning
Wang, L., Lei, B., Li, Q., Su, H., Zhu, J., & Zhong, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3111019 · Cited by 57
AFEC: Active Forgetting of Negative Transfer in Continual LearningPreprint
Wang, L., Zhang, M., Jia, Z., Li, Q., Bao, C., Ma, K., Zhu, J., & Zhong, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.12187 · Cited by 54
Shi, W., & Ye, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00170 · Cited by 54
Tahir, G. A., & Loo, C. K. (2020) · IEEE Access · doi:10.1109/access.2020.2991810 · Cited by 53
Image De-raining via Continual Learning
Zhou, M., Xiao, J., Chang, Y., Fu, X., Liu, A., Pan, J., & Zha, Z.-J. (2021) · 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/cvpr46437.2021.00487 · Cited by 52
Graph Few-shot Class-incremental Learning
Tan, Z., Ding, K., Guo, R., & Liu, H. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498455 · Cited by 54
Lifelong nnU-Net: a framework for standardized medical continual learning
González, C., Ranem, A., dos Santos, D. P., Othman, A. E., & Mukhopadhyay, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-34484-2 · Cited by 52
Ahmed, N., Kukleva, A., & Schiele, B. (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/cvpr52733.2024.02717 · Cited by 52
Wang, C., Qiu, Y., Gao, D., & Scherer, S. (2022) · 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/cvpr52688.2022.01335 · Cited by 57
Endpoints Weight Fusion for Class Incremental Semantic Segmentation
Xiao, J., Zhang, C., Feng, J., Liu, X., van de Weijer, J., & Cheng, M. (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/cvpr52729.2023.00696 · Cited by 51
Peng, J., Tang, B., Jiang, H., Li, Z., Lei, Y., Lin, T., & Li, H. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3056201 · Cited by 53
Class-Incremental Novel Class Discovery
Roy, S., Liu, M., Zhong, Z., Sebe, N., & Ricci, E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19827-4_19 · Cited by 50
Cognitively-Inspired Model for Incremental Learning Using a Few Examples
Ayub, A., & Wagner, A. R. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00119 · Cited by 50
CPR: Classifier-Projection Regularization for Continual LearningPreprint
Cha, S., Hsu, H., Hwang, T., Calmon, F. P., & Moon, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07326 · Cited by 50
Benchmarking Class Incremental Learning in Deep Learning Traffic Classification
Bovenzi, G., Nascita, A., Yang, L., Finamore, A., Aceto, G., Ciuonzo, D., Pescapè, A., & Rossi, D. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3287430 · Cited by 48
Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning
Kim, S., Noci, L., Orvieto, A., & Hofmann, T. (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/cvpr52729.2023.01148 · Cited by 48
Unsupervised Continual Anomaly Detection with Contrastively-Learned Prompt
Liu, J., Wu, K., Nie, Q., Chen, Y., Gao, B.-B., Liu, Y., Wang, J., Wang, C., & Zheng, F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28153 · Cited by 52
Kaushik, P., Gain, A., Kortylewski, A., & Yuille, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11343 · Cited by 67
Learning with Selective Forgetting
Shibata, T., Irie, G., Ikami, D., & Mitsuzumi, Y. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/137 · Cited by 47
Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers
Cha, S., Cho, S., Hwang, D., Lee, H., Moon, T., & Lee, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28996 · Cited by 59
A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application
Yuan, B., & Zhao, D. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3446949 · Cited by 59
SDDGR: Stable Diffusion-Based Deep Generative Replay for Class Incremental Object Detection
Kim, J., Cho, H., Kim, J., Tiruneh, Y. Y., & Baek, 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/cvpr52733.2024.02718 · Cited by 55
Wittkuhn, L., Chien, S., Hall-McMaster, S., & Schuck, N. W. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.08.002 · Cited by 49
Continual Semantic Segmentation with Automatic Memory Sample Selection
Zhu, L., Chen, T., Yin, J., See, S. C. W., & Liu, J. (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/cvpr52729.2023.00301 · Cited by 51
CLeaR: An adaptive continual learning framework for regression tasks
He, Y., & Sick, B. (2021) · AI Perspectives · doi:10.1186/s42467-021-00009-8 · Cited by 46
Improved Schemes for Episodic Memory-based Lifelong LearningPreprint
Guo, Y., Liu, M., Yang, T., & Rosing, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11763 · Cited by 46
Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling
Yang, Y., Zhou, D.-W., Zhan, D.-C., Xiong, H., Jiang, Y. Y., & Yang, J. J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3132622 · Cited by 46
Multi-Task Incremental Learning for Object DetectionPreprint
Liu, X., Yang, H., Ravichandran, A., Bhotika, R., & Soatto, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05347 · Cited by 45
Incremental learning with neural networks for computer vision: a survey
Liu, H., Zhou, Y. Z., Liu, B., Zhao, J., Yao, R., & Shao, Z. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10294-2 · Cited by 49
Anchor Assisted Experience Replay for Online Class-Incremental Learning
Lin, H., Feng, S., Li, X., Li, W., & Ye, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3219605 · Cited by 44
Survey on Online Streaming Continual Learning
Gunasekara, N., Pfahringer, B., Gomes, H. M., & Bifet, A. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/743 · Cited by 44
Shang, C., Li, H., Meng, F., Wu, Q., Qiu, H., & Wang, L. (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/cvpr52729.2023.00697 · Cited by 47
Li, D., Qi, J., Hong, S., Deng, P., & Sun, H. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3343193 · Cited by 44
Federated continual learning: Concepts, challenges, and solutions
Hamedi, P., Razavi‐Far, R., & Hallaji, E. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130844 · Cited by 55
RODEO: Replay for Online Object Detection
Acharya, M., Hayes, T. L., & Kanan, C. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.128 · Cited by 55
Chauhan, J., Kwon, Y. D., Hui, P., & Mascolo, C. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3432203 · Cited by 44
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
Farquhar, S., Osborne, M., & Gal, Y. (2019) · arXiv · doi:10.48550/arxiv.1907.00865 · Cited by 43
A Few-Shot Class-Incremental Learning Approach for Intrusion Detection
Wang, T., Lv, Q., Hu, B., & Sun, D. (2021) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn52240.2021.9522260 · Cited by 43
Bring Evanescent Representations to Life in Lifelong Class Incremental Learning
Toldo, M., & Özay, M. (2022) · 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/cvpr52688.2022.01623 · Cited by 43
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental LearningPreprint
Zhou, P., Mai, L., Zhang, J., Xu, N., Wu, Z., & Davis, L. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01769 · Cited by 43
Continual Unsupervised Domain Adaptation in Data-Constrained Environments
Taufique, A. M. N., Jahan, C. S., & Savakis, A. E. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3233791 · Cited by 44
Computationally Budgeted Continual Learning: What Does Matter?
Prabhu, A., Hammoud, H. A. A. K., Dokania, P. K., Torr, P. H. S., Lim, S.-N., Ghanem, B., & Bibi, A. (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/cvpr52729.2023.00360 · Cited by 48
Self-citationIncremental SAR Automatic Target Recognition With Error Correction and High Plasticity
Tang, J., Xiang, D., Zhang, F., Ma, F., Zhou, Y., & Li, H.-C. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3141485 · Cited by 42
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Yang, G., Fini, E., Xu, D., Rota, P., Ding, M., Tang, H., Alameda-Pineda, X., & Ricci, E. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3167555 · Cited by 42
Boschini, M., Bonicelli, L., Porrello, A., Bellitto, G., Pennisi, M., Palazzo, S., Spampinato, C., & Calderara, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20050-2_40 · Cited by 49
Meta-Consolidation for Continual LearningPreprint
Joseph, K. J., & Balasubramanian, V. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00352 · Cited by 43
Continual Learning for Smart City: A Survey
Yang, L., Luo, Z., Zhang, S., Teng, F., & Li, T. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3447123 · Cited by 46
Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting
Zhang, B., Guo, Y., Li, Y., He, Y., Wang, H., & Dai, Q. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3099700 · Cited by 42
Self-Supervised Training Enhances Online Continual Learning
Gallardo, G. J., Hayes, T. L., & Kanan, C. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.181 · Cited by 41
EEC: Learning to Encode and Regenerate Images for Continual LearningPreprint
Ayub, A., & Wagner, A. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04904 · Cited by 41
Curiosity-Driven Class-Incremental Learning via Adaptive Sample Selection
Hu, Q., Gao, Y., & Cao, B. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3196092 · Cited by 40
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Gomez-Villa, A., Twardowski, B., Lu, Y., Bagdanov, A. D., & van de Weijer, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00432 · Cited by 44
General Incremental Learning with Domain-aware Categorical Representations
Xie, J., Yan, S., & He, X. (2022) · 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/cvpr52688.2022.01395 · Cited by 40
Real-Time Evaluation in Online Continual Learning: A New Hope
Ghunaim, Y., Bibi, A., Alhamoud, K., Alfarra, M., Hammoud, H. A. A. K., Prabhu, A., Torr, P. H. S., & Ghanem, B. (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/cvpr52729.2023.01144 · Cited by 52
Self-citationContinual Learning by Asymmetric Loss Approximation With Single-Side Overestimation
Park, D.-M., Hong, S., Han, B., & Lee, K. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00343 · Cited by 40
Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation
Zhai, M., Chen, L., & Mori, G. (2021) · 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/cvpr46437.2021.00228 · Cited by 40
Learning Adaptive Embedding Considering Incremental Class
Yang, Y., Sun, Z.-Q., Zhu, H., Fu, Y., Zhou, Y., Xiong, H., & Yang, J. J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3109131 · Cited by 39
Towards Continual Adaptation in Industrial Anomaly Detection
Li, W., Zhan, J., Wang, J., Xia, B., Gao, B.-B., Liu, J., Wang, C., & Zheng, F. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548232 · Cited by 40
VQACL: A Novel Visual Question Answering Continual Learning Setting
Zhang, X., Zhang, F., & Xu, C. (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/cvpr52729.2023.01831 · Cited by 42
Dynamic Residual Classifier for Class Incremental Learning
Chen, X., & Chang, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01718 · Cited by 42
Audio-Visual Class-Incremental Learning
Pian, W., Mo, S., Guo, Y., & Tian, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00717 · Cited by 39
Continual Learning With Structured Inheritance for Semantic Segmentation in Aerial Imagery
Feng, Y., Sun, X., Diao, W., Li, J., Gao, X., & Fu, K. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3076664 · Cited by 39
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
Choi, Y., El‐Khamy, M., & Lee, J. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00393 · Cited by 39
Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification
Liu, W., Nie, X., Zhang, B., & Sun, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3173995 · Cited by 40
CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One
Wang, L., Zhang, X., Li, Q., Zhu, J., & Zhong, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_15 · Cited by 38
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
Liu, B., Yang, B., Xie, L., Wang, R., Tian, Q. C., & Ye, Q. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3273291 · Cited by 38
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Mundt, M., Pliushch, I., Majumder, S., Hong, Y., & Ramesh, V. (2022) · Journal of Imaging · doi:10.3390/jimaging8040093 · Cited by 41
Greco, C., Plank, B., Fernández, R., & Raffaella Bernardi, B. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1350 · Cited by 38
Online Depth Learning Against Forgetting in Monocular Videos
Zhang, Z., Lathuilière, S., Ricci, E., Sebe, N., Yan, Y., & Yang, J. J. (2020) · 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/cvpr42600.2020.00455 · Cited by 38
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
Ashok, A., Joseph, K. J., & Balasubramanian, V. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_7 · Cited by 40
Shi, Y., Shi, D., Qiao, Z., Wang, Z., Zhang, Y., Yang, S., & Qiu, C. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.05.006 · Cited by 38
Metadata
- Authors
- Chaudhry, A., Dokania, P. K., Ajanthan, T., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Ajanthan, Thalaiyasingam
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 556-572
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2786446225
- Cited by
- 1,091
- References
- 33
- Citations by year
- 2026: 78 · 2025: 182 · 2024: 217 · 2023: 189 · 2022: 135 · 2021: 154 · 2020: 84 · 2019: 45 · 2018: 7
- FWCI
- 29.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/N019474/1, EP/M013774/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- incremental learning, catastrophic forgetting, intransigence, path integral, Kullback-Leibler divergence, knowledge preservation, classifier updating
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
