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
A Continual Learning Survey: Defying Forgetting in Classification Tasks
Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., & Tuytelaars, T. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3057446 · Cited by 1,647
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Wang, L., Zhang, X., Su, H., & Zhu, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367329 · Cited by 1,113
“Besides, weight regularization can also be combined with experience replay for better performance and generality [44], [52].”
BackgroundMethodInfluentialEfficient Lifelong Learning with A-GEM
Chaudhry, A., Ranzato, M., Rohrbach, M., & Elhoseiny, M. (2018) · arXiv · doi:10.48550/arxiv.1812.00420 · Cited by 852
Self-citationLearning to Prompt for Continual Learning
Wang, Z., Zhang, Z., Lee, C., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., & Pfister, T. (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/cvpr52688.2022.00024 · Cited by 936
DER: Dynamically Expandable Representation for Class Incremental Learning
Yan, S., Xie, J., & He, X. (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/cvpr46437.2021.00303 · Cited by 803
Class-Incremental Learning: Survey and Performance Evaluation on Image Classification
Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A. D., & van de Weijer, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3213473 · Cited by 740
Three scenarios for continual learningPreprint
van de Ven, G. M., & Tolias, A. S. (2019) · Lirias · doi:10.48550/arxiv.1904.07734 · Cited by 1,042
Dhar, P., Singh, R., Peng, K., Wu, Z., & Chellappa, R. (2019) · 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/cvpr.2019.00528 · Cited by 548
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing InterferencePreprint
Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., & Tesauro, G. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11910 · Cited by 534
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., & Díaz-Rodríguez, N. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.12.004 · Cited by 593
On Tiny Episodic Memories in Continual LearningPreprint
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., & Ranzato, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10486 · Cited by 506
Self-citationGDumb: A Simple Approach that Questions Our Progress in Continual Learning
Prabhu, A., Torr, P. H. S., & Dokania, P. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_31 · Cited by 524
Self-citationPrototype Augmentation and Self-Supervision for Incremental Learning
Zhu, F., Zhang, X.-Y., Wang, C., Yin, F., & Liu, C.-L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00581 · Cited by 427
Online continual learning in image classification: An empirical survey
Mai, Z., Li, R., Jeong, J., Quispe, D., Kim, H., & Sanner, S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.021 · Cited by 468
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion
Douillard, A., Ramé, A., Couairon, G., & Cord, 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/cvpr52688.2022.00907 · Cited by 417
Class-Incremental Learning: A Survey
Zhou, D.-W., Wang, Q.-W., Qi, Z.-H., Ye, H.-J., Zhan, D.-C., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3429383 · Cited by 376
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Bang, J., Kim, H., Yoo, Y., Ha, J.-W., & Choi, 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/cvpr46437.2021.00812 · Cited by 376
Modeling the Background for Incremental Learning in Semantic Segmentation
Cermelli, F., Mancini, M., Bulò, S. R., Ricci, E., & Barbara Caputo, B. (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/cvpr42600.2020.00925 · Cited by 364
Class-incremental Learning via Deep Model Consolidation
Zhang, J., Zhang, J., Ghosh, S., Li, D., Taşcı, Ş., Heck, L. P., Zhang, H., & Kuo, C. ‐. J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093365 · Cited by 361
Semantic Drift Compensation for Class-Incremental Learning
Yu, L., Twardowski, B., Liu, X., Herranz, L., Wang, K., Cheng, Y., Jui, S., & van de Weijer, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00701 · Cited by 340
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning
Ostapenko, O., Puscas, M., Klein, T., Jähnichen, P., & Nabi, M. (2019) · 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/cvpr.2019.01158 · Cited by 294
PLOP: Learning without Forgetting for Continual Semantic Segmentation
Douillard, A., Chen, Y., Dapogny, A., & Cord, M. (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/cvpr46437.2021.00403 · Cited by 290
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
Liu, X., Masana, M., Herranz, L., van de Weijer, J., López, A. M., & Bagdanov, A. D. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545895 · Cited by 291
REMIND Your Neural Network to Prevent Catastrophic Forgetting
Hayes, T. L., Kafle, K., Shrestha, R., Acharya, M., & Kanan, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_28 · Cited by 286
Incremental Learning Techniques for Semantic Segmentation
Michieli, U., & Zanuttigh, P. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00400 · Cited by 274
Adaptive Aggregation Networks for Class-Incremental Learning
Liu, Y., Schiele, B., & Sun, Q. (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/cvpr46437.2021.00257 · Cited by 245
Co 2 L: Contrastive Continual Learning
Cha, H., Lee, J., & Shin, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00938 · Cited by 243
(2019) · arXiv (Cornell University) · Cited by 214
Self-citationOvercoming Catastrophic Forgetting With Unlabeled Data in the Wild
Lee, K., Lee, K., Shin, J., & Lee, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00040 · Cited by 218
Using Hindsight to Anchor Past Knowledge in Continual Learning
Chaudhry, A., Gordo, A., Dokania, P. K., Torr, P. H. S., & López-Paz, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16861 · Cited by 208
Self-citationCompacting, Picking and Growing for Unforgetting Continual LearningPreprint
Hung, S. C. Y., Tu, C.-H., Wu, C.-E. R., Chen, C., Chan, Y.-M., & Chen, C.-S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06562 · Cited by 204
Online Class-Incremental Continual Learning with Adversarial Shapley Value
Shim, D., Mai, Z., Jeong, J., Sanner, S., Kim, H., & Jang, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17159 · Cited by 203
Towards Robust Evaluations of Continual LearningPreprint
Farquhar, S., & Gal, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09733 · Cited by 275
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Cheraghian, A., Rahman, S., Fang, P., Roy, S. K., Petersson, L., & Harandi, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00256 · Cited by 201
Distilling Causal Effect of Data in Class-Incremental Learning
Hu, X., Tang, K., Miao, C., Hua, X., & Zhang, H. (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/cvpr46437.2021.00395 · Cited by 201
SS-IL: Separated Softmax for Incremental Learning
Ahn, H., Kwak, J., Lim, S., Bang, H., Kim, H., & Moon, T. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00088 · Cited by 210
Mai, Z., Li, R., Kim, H., & Sanner, S. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00398 · Cited by 197
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
Zhang, J. O., (Sasha), A. S., Zamir, A., Guibas, L. J., & Malik, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_41 · Cited by 212
Continual Lifelong Learning in Natural Language Processing: A Survey
Biesialska, M., Biesialska, K., & Costa‐jussà, M. R. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.574 · Cited by 244
Lifelong GAN: Continual Learning for Conditional Image Generation
Zhai, M., Chen, L., Tung, F., He, J., Nawhal, M., & Mori, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00285 · Cited by 188
Incremental Learning Using Conditional Adversarial Networks
Xiang, Y., Fu, Y., Ji, P., & Huang, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00672 · Cited by 190
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Chen, S., Hou, Y., Cui, Y., Che, W., Liu, T., & Yu, X. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.634 · Cited by 226
Adversarial Continual Learning
Ebrahimi, S., Meier, F., Calandra, R., Darrell, T., & Rohrbach, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_23 · Cited by 188
Loss of plasticity in deep continual learning
Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172
“[20] observed loss of plasticity in continual image classification problems.”
Memory-Efficient Incremental Learning Through Feature Adaptation
İşcen, A., Zhang, J., Lazebnik, S., & Schmid, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_41 · Cited by 176
Michieli, U., & Zanuttigh, P. (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/cvpr46437.2021.00117 · Cited by 164
Self-Supervised Models are Continual Learners
Fini, E., da Costa, V. G. T., Alameda-Pineda, X., Ricci, E., Alahari, K., & Mairal, 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/cvpr52688.2022.00940 · Cited by 155
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
Hayes, T. L., & Kanan, C. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00118 · Cited by 148
Mundt, M., Hong, Y., Pliushch, I., & Ramesh, V. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.014 · Cited by 167
iTAML: An Incremental Task-Agnostic Meta-learning Approach
Rajasegaran, J., Khan, S. H., Hayat, M., Khan, F. S., & Shah, M. A. (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/cvpr42600.2020.01360 · Cited by 147
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
Zhou, F., & Cao, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16602 · Cited by 156
Generative Feature Replay For Class-Incremental Learning
Liu, X., Wu, C., Menta, M., Herranz, L., Raducanu, B., Bagdanov, A. D., Jui, S., & van de Weijer, J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00121 · Cited by 142
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesPreprint
Requeima, J., Gordon, J. A. R., Bronskill, J. F., Nowozin, S., & Turner, R. E. (2019) · arXiv (Cornell University) · doi:10.17863/cam.47574 · Cited by 134
Class-Incremental Continual Learning Into the eXtended DER-Verse
Boschini, M., Bonicelli, L., Buzzega, P., Porrello, A., & Calderara, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3206549 · Cited by 133
On Learning the Geodesic Path for Incremental Learning
Simon, C., Koniusz, P., & Harandi, M. T. (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/cvpr46437.2021.00164 · Cited by 132
Continual learning for recurrent neural networks: An empirical evaluation
Cossu, A., Carta, A., Lomonaco, V., & Bacciu, D. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.021 · Cited by 129
Training Networks in Null Space of Feature Covariance for Continual Learning
Wang, S., Li, X., Sun, J., & Xu, Z. (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/cvpr46437.2021.00025 · Cited by 135
Representation Compensation Networks for Continual Semantic Segmentation
Zhang, C., Xiao, J.-W., Liu, X., Chen, Y.-C., & Cheng, 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/cvpr52688.2022.00692 · Cited by 133
Continual Learning, Fast and Slow
Pham, Q., Liu, C., & Hoi, S. C. H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3324203 · Cited by 135
Understanding the Role of Training Regimes in Continual LearningPreprint
Mirzadeh, S. I., Farajtabar, M., Pascanu, R., & Ghasemzadeh, H. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06958 · Cited by 123
GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning
Tiwari, R., Killamsetty, K., Iyer, R., & Shenoy, P. (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/cvpr52688.2022.00020 · Cited by 122
An Appraisal of Incremental Learning Methods
Luo, Y., Yin, L., Bai, W., & Mao, K. (2020) · Entropy · doi:10.3390/e22111190 · Cited by 120
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Xie, G., Wang, J., Liu, J., Lyu, J., Liu, Y., Wang, C., Zheng, F., & Jin, Y. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3357213 · Cited by 140
Rethinking Experience Replay: a Bag of Tricks for Continual Learning
Buzzega, P., Boschini, M., Porrello, A., & Calderara, S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412614 · Cited by 126
MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning
Zhao, H., Fu, Y., Kang, M., Tian, Q., Wu, F., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3133897 · Cited by 115
Memory-Efficient Class-Incremental Learning for Image Classification
Zhao, H., Wang, H., Fu, Y., Wu, F. Q., & Li, X. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3072041 · Cited by 120
Jha, A. K., Bradshaw, T., Buvat, I., Hatt, M., Prabhat, K. C., Liu, C., Obuchowski, N., Saboury, B., Slomka, P. J., Sunderland, J. J., et al. (2022) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.263239 · Cited by 117
Online Continual Learning with Maximally Interfered Retrieval
Aljundi, R., Caccia, L., Belilovsky, E., Caccia, M., Lin, M., Charlin, L., & Tuytelaars, T. (2019) · arXiv · doi:10.48550/arxiv.1908.04742 · Cited by 140
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Hayes, T. L., Krishnan, G. P., Bazhenov, M., Siegelmann, H. T., Sejnowski, T. J., & Kanan, C. (2021) · Neural Computation · doi:10.1162/neco_a_01433 · Cited by 106
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
Cao, Y., Peng, H. M., Wu, J. X., Dou, Y., Li, J., & Yu, P. S. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449834 · Cited by 101
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning
Wang, Z., Yang, E., Shen, L., & Huang, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3498346 · Cited by 132
Selfless Sequential LearningPreprint
Aljundi, R., Rohrbach, M., & Tuytelaars, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05421 · Cited by 114
Knowledge distillation for incremental learning in semantic segmentation
Michieli, U., & Zanuttigh, P. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103167 · Cited by 100
Task-Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates
Zeno, C., Golan, I., Hoffer, E., & Soudry, D. (2021) · Neural Computation · doi:10.1162/neco_a_01430 · Cited by 145
Imbalanced Continual Learning with Partitioning Reservoir Sampling
Kim, C. D., Jeong, J., & Kim, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_25 · Cited by 101
Episodic Memory in Lifelong Language Learning
d'Autume, C. D. M., Ruder, S., Kong, L., & Yogatama, D. (2019) · arXiv · doi:10.48550/arxiv.1906.01076 · Cited by 95
Toward Training Recurrent Neural Networks for Lifelong Learning
Sodhani, S., Chandar, S., & Bengio, Y. (2019) · Neural Computation · doi:10.1162/neco_a_01246 · Cited by 99
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Liu, Y., Parisot, S., Slabaugh, G., Jia, X., Leonardis, A., & Tuytelaars, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_42 · Cited by 96
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence
Wang, L., Zhang, X., Li, Q., Zhang, M., Su, H., Zhu, J., & Zhong, Y. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00747-w · Cited by 91
IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection
Liu, L., Kuang, Z., Chen, Y., Xue, J.-H., Yang, W., & Zhang, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3002583 · Cited by 90
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation
Zhang, J., Chen, C., Zhuang, W., & Lyu, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00441 · Cited by 90
Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation
Yang, G., Fini, E., Xu, D., Rota, P., Ding, M., Nabi, M., Alameda-Pineda, X., & Ricci, E. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3163806 · Cited by 94
LIQA: Lifelong Blind Image Quality Assessment
Liu, J., Zhou, W., Li, X., Xu, J., & Chen, Z. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3190700 · Cited by 85
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability
Yang, Y., Zhou, D.-W., Zhan, D.-C., Xiong, H., & Jiang, Y. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330865 · Cited by 83
Gu, Y., Yang, X., Wei, K., & Deng, C. D. (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/cvpr52688.2022.00729 · Cited by 82
Continual Learning with Node-Importance based Adaptive Group Sparse RegularizationPreprint
Jung, S., Ahn, H., Cha, S., & Moon, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13726 · Cited by 86
Wang, L., Yang, K., Li, C., Hong, L., Li, Z., & Zhu, 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/cvpr46437.2021.00534 · Cited by 81
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual LearningPreprint
Caccia, M., Rodríguez, P., Ostapenko, O., Normandin, F., Lin, M., Caccia, L., Laradji, I., Rish, I., Lacoste, A., Vázquez, D., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05856 · Cited by 102
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
Volpi, R., Larlus, D., & Rogez, 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/cvpr46437.2021.00442 · Cited by 81
Dynamic Support Network for Few-Shot Class Incremental Learning
Yang, B., Lin, M., Zhang, Y., Liu, B., Liang, X., Ji, R., & Ye, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3175849 · Cited by 80
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
You, C., Xiang, J., Su, K., Zhang, X., Dong, S., Onofrey, J. A., Staib, L. H., & Duncan, J. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18523-6_1 · Cited by 80
Recent Advances of Continual Learning in Computer Vision: An Overview
Qu, H., Rahmani, H., Xu, L., Williams, B. M., & Liu, J. (2025) · IET Computer Vision · doi:10.1049/cvi2.70013 · Cited by 122
Continual Learning for Text Classification with Information Disentanglement Based Regularization
Huang, Y., Zhang, Y., Chen, J., Wang, X., & Yang, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.218 · Cited by 74
MetaSearch: Incremental Product Search via Deep Meta-Learning
Wang, Q., Liu, X., Liu, W., Liu, A.-A., Liu, W., & Mei, T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3004249 · Cited by 74
Continual Deep Learning by Functional Regularisation of Memorable PastPreprint
Pan, P., Swaroop, S., Immer, A., Eschenhagen, R., Turner, R. E., & Khan, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14070 · Cited by 74
Continual Learning: A Review of Techniques, Challenges, and Future Directions
Wickramasinghe, B., Saha, G., & Roy, K. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3339091 · Cited by 82
Toward Understanding Catastrophic Forgetting in Continual LearningPreprint
Nguyen, C. V., Achille, A., Lam, M., Hassner, T., Mahadevan, V., & Soatto, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01091 · Cited by 73
Learning Without Forgetting for Vision-Language Models
Zhou, D.-W., Zhang, Y., Wang, Y., Ning, J., Ye, H.-J., Zhan, D., & Liu, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3540889 · Cited by 102
Self-Training for Class-Incremental Semantic Segmentation
Yu, L., Liu, X., & van de Weijer, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3155746 · Cited by 70
Continual Learning with Lifelong Vision Transformer
Wang, Z., Liu, L., Duan, Y., Kong, Y., & Tao, D. (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/cvpr52688.2022.00027 · Cited by 76
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
