Cited by 325Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsReinforcement Learning in Robotics

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Encoder Based Lifelong Learning

Open access

Rannen, Amal, Aljundi, Rahaf, Blaschko, Matthew B., Tuytelaars, Tinne·2017·2017 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2017.148

Open this paper in CoChat — free account Open PDF

Abstract

This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning a new one by using autoencoders. For each task, an under-complete autoencoder is learned, capturing the features that are crucial for its achievement. When a new task is presented to the system, we prevent the reconstructions of the features with these autoencoders from changing, which has the effect of preserving the information on which the previous tasks are mainly relying. At the same time, the features are given space to adjust to the most recent environment as only their projection into a low dimension submanifold is controlled. The proposed system is evaluated on image classification tasks and shows a reduction of forgetting over the state-ofthe-art.

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

Crossref · 10.1109/iccv.2017.148

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019

Show all 23 references

Research works printed but not linked: 5

mitchell, 1980, The need for biases in learning generalizations
pentina, 2015, Lifelong learning with non-iid tasks, Advances in neural information processing systems, 1540
welinder, 2010, Caltech-UCSD Birds 200, Technical Report CNS-TR-2010-001
everingham, 0, The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
goodfellow, 2016, Deep Learning

Sorted by citations

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
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
Self-citation
Memory Aware Synapses: Learning What (not) to Forget
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., & Tuytelaars, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_9 · Cited by 1,590
Self-citation
Large Scale Incremental Learning
Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., & Fu, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00046 · Cited by 1,313
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
Mallya, A., & Lazebnik, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00810 · Cited by 1,258
End-to-End Incremental Learning
Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C., & Alahari, K. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01258-8_15 · Cited by 1,184
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
“LwM [119] exploits the attention maps of new training samples for KD. EBLL [121] learns task-speciﬁc autoencoders and prevents changes in feature reconstruction.”
Background
Learning a Unified Classifier Incrementally via Rebalancing
Hou, S., Pan, X., Loy, C. C., Wang, Z., & Lin, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00092 · Cited by 1,195
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
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification
Wang, J., Zhu, X., Gong, S., & Li, W. (2018) · 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.2018.00242 · Cited by 671
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
Mallya, A., Davis, D., & Lazebnik, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_5 · Cited by 673
Measuring Catastrophic Forgetting in Neural Networks
Kemker, R., McClure, M., Abitino, A., Hayes, T., & Kanan, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11651 · Cited by 672
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion
Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K., & Kautz, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00874 · Cited by 540
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
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
GDumb: 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
Embracing Change: Continual Learning in Deep Neural Networks
Hadsell, R., Rao, D., Rusu, A. A., & Pascanu, R. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.09.004 · Cited by 467
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Wu, C.-S., Madotto, A., Hosseini-Asl, E., Xiong, C., Socher, R., & Fung, P. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1078 · Cited by 470
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
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
Task-Free Continual Learning
Aljundi, R., Kelchtermans, K., & Tuytelaars, T. (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.2019.01151 · Cited by 335
Self-citation
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
Overcoming 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
Rethinking the Distribution Gap of Person Re-identification with Camera-Based Batch Normalization
Zhuang, Z., Wei, L., Xie, L., Zhang, T., Zhang, H., Wu, H., Ai, H., & Tian, Q. C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58610-2_9 · 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
Lifelong Learning via Progressive Distillation and Retrospection
Hou, S., Pan, X., Loy, C. C., Wang, Z., & Lin, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_27 · Cited by 190
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
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
Incremental Learning in Online Scenario
He, J., Mao, R., Shao, Z., & Zhu, F. M. (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/cvpr42600.2020.01394 · Cited by 188
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 223
Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
Taşar, O., Tarabalka, Y., & Alliez, P. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2925416 · Cited by 144
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Mundt, M., Hong, Y., Pliushch, I., & Ramesh, V. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.014 · Cited by 167
An Appraisal of Incremental Learning Methods
Luo, Y., Yin, L., Bai, W., & Mao, K. (2020) · Entropy · doi:10.3390/e22111190 · Cited by 120
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
What and How: Generalized Lifelong Spectral Clustering via Dual Memory
Sun, G., Cong, Y., Dong, J., Liu, Y., Ding, Z., & Yu, H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3058852 · Cited by 106
Continual Learning for Blind Image Quality Assessment
Zhang, W., Li, D., Ma, C., Zhai, G., Yang, X., & Ma, K. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3178874 · Cited by 112
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
Self-citation
Selfless Sequential LearningPreprint
Aljundi, R., Rohrbach, M., & Tuytelaars, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05421 · Cited by 114
Self-citation
Learning Latent Representations Across Multiple Data Domains Using Lifelong VAEGAN
Ye, F., & Borş, A. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_46 · Cited by 76
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
Dense Network Expansion for Class Incremental Learning
Hu, Z., Li, Y., Lyu, J., Gao, D., & Vasconcelos, N. 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/cvpr52729.2023.01141 · Cited by 82
Continual Learning for Named Entity Recognition
Monaikul, N., Castellucci, G., Filice, S., & Rokhlenko, O. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17600 · Cited by 71
Multi-View correlation distillation for incremental object detection
Yang, D., Zhou, Y., Zhang, A., Sun, X., Wu, D., Wang, W., & Ye, Q. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108863 · Cited by 70
Continual learning for predictive maintenance: Overview and challenges
Hurtado, J., Salvati, D., Semola, R., Bosio, M., & Lomonaco, V. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200251 · Cited by 70
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
Representative Task Self-Selection for Flexible Clustered Lifelong Learning
Sun, G., Cong, Y., Wang, Q., Zhong, B., & Fu, Y. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3042500 · Cited by 68
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
Lessons from infant learning for unsupervised machine learning
Zaadnoordijk, L., Besold, T. R., & Cusack, R. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00488-2 · Cited by 65
An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions
Mirza, M. J., Masana, M., Possegger, H., & Bischof, H. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00339 · Cited by 71
Efficient Parametrization of Multi-domain Deep Neural Networks
Rebuffi, S.-A., Vedaldi, A., & Bilen, H. (2018) · 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.2018.00847 · Cited by 75
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
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
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Chen, H., Wang, J., Shao, K., Liu, F., Hao, J., Guan, C., Chen, G., & Heng, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00767 · Cited by 69
Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
Aleixo, E. L., Colonna, J. G., Cristo, M., & Fernandes, E. (2023) · arXiv · doi:10.48550/arxiv.2312.10549 · Cited by 54
LIL: Lightweight Incremental Learning Approach Through Feature Transfer for Remote Sensing Image Scene Classification
Lü, X., Sun, X., Diao, W., Feng, Y., Wang, P., & Fu, K. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3102629 · Cited by 54
A review of machine learning with small and limited data
Naser, M. Z. (2026) · Journal Of Big Data · doi:10.1186/s40537-025-01346-9 · Cited by 82
Revisiting Distillation and Incremental Classifier Learning
Javed, K., & Shafait, F. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-20876-9_1 · Cited by 53
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 52
Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction
Wang, B., Zhang, Y., Wang, X., Wang, P., Zhou, Z., Bai, L., & Wang, Y. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599463 · Cited by 50
Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
Aleixo, E. L., Colonna, J. G., Cristo, M., & Fernandes, E. (2024) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2024.3966 · Cited by 54
Scarcity of Labels in Non-Stationary Data Streams: A Survey
Fahy, C., Yang, S., & Góngora, M. (2022) · ACM Computing Surveys · doi:10.1145/3494832 · Cited by 53
Lifelong Visual-Tactile Cross-Modal Learning for Robotic Material Perception
Zheng, W., Liu, H., & Sun, F. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2980892 · Cited by 45
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
Dynamically Expandable Graph Convolution for Streaming Recommendation
He, B., He, X. D., Zhang, Y., Tang, R., & Ma, C. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583237 · Cited by 51
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
The Challenges of Continuous Self-Supervised Learning
Purushwalkam, S., Morgado, P. M., & Gupta, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_40 · Cited by 47
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
Lifelong robotic visual-tactile perception learning
Dong, J., Cong, Y., Sun, G., & Zhang, T. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108176 · Cited by 38
Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations
Yan, Z., Tian, Y., Shi, X., Guo, P. Y., Wang, P., & Zha, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01549 · Cited by 40
Open World Object Detection: A Survey
Li, Y., Wang, Y., Wang, W., Lin, D., Li, B., & Yap, K. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3480691 · Cited by 46
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
Guirguis, K., Hendawy, A., Eskandar, G., Abdelsamad, M., Kayser, M., & Beyerer, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00449 · Cited by 36
Online Hyperparameter Optimization for Class-Incremental Learning
Liu, Y., Li, Y., Schiele, B., & Sun, Q. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26070 · Cited by 36
Survey of continuous deep learning methods and techniques used for incremental learning
Leo, J., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127545 · Cited by 37
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
Hung, S. C. Y., Lee, J., Wan, T. S. T., Chen, C., Chan, Y.-M., & Chen, C. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3325053 · Cited by 35
Online Continual Learning with Contrastive Vision Transformer
Wang, Z., Liu, L., Kong, Y., Guo, J., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_36 · Cited by 38
EETC: An extended encrypted traffic classification algorithm based on variant resnet network
Ma, X., Zhu, W., Wei, J., Jin, Y., Gu, D., & Wang, R. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103175 · Cited by 34
NetTailor: Tuning the Architecture, Not Just the Weights
Morgado, P. M., & Vasconcelos, N. 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.2019.00316 · Cited by 34
Convolutional Neural Network With Developmental Memory for Continual Learning
Park, G.-M., Yoo, S.-M., & Kim, J.-H. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007548 · Cited by 33
Continual Learning through Retrieval and Imagination
Wang, Z., Liu, L., Duan, Y., & Tao, D. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20837 · Cited by 34
Continual Learning for Visual Search with Backward Consistent Feature Embedding
Wan, T. S. T., Chen, J.-C., Wu, T.-Y., & Chen, C.-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/cvpr52688.2022.01620 · Cited by 32
Contrastive Correlation Preserving Replay for Online Continual Learning
Yu, D.-P., Zhang, M., Li, M., Zha, F., Zhang, J., Sun, L., & Huang, K. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3285221 · Cited by 33
Overcoming Catastrophic Forgetting in Continual Learning by Exploring Eigenvalues of Hessian Matrix
Kong, Y., Liu, L., Chen, H., Kacprzyk, J., & Tao, D. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3292359 · Cited by 37
Camera-Based Batch Normalization: An Effective Distribution Alignment Method for Person Re-Identification
Zhuang, Z., Wei, L., Xie, L., Ai, H., & Tian, Q. C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3058111 · Cited by 32
Open-world Semantic Segmentation for LIDAR Point Clouds
Cen, J., Peng, Y., Zhang, S., Cai, J., Luan, D., Tang, M., Liu, M., & Wang, M. Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_19 · Cited by 32
Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning
Kong, Y., Liu, L., Wang, Z., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_13 · Cited by 32
Efficient Multi-Domain Learning by Covariance Normalization
Li, Y., & Vasconcelos, N. 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.2019.00557 · Cited by 33
In Defense of the Learning Without Forgetting for Task Incremental Learning
Oren, G., & Wolf, L. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00250 · Cited by 31
Self-Paced Weight Consolidation for Continual Learning
Cong, W. W., Cong, Y., Sun, G., Liu, Y., & Dong, J. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3304567 · Cited by 31
Analogical Learning-Based Few-Shot Class-Incremental Learning
Li, J., Dong, S., Gong, Y., He, Y., & Wei, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3350913 · Cited by 32
Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem
Lange, M. D., Jia, X., Parisot, S., Leonardis, A., Slabaugh, G., & Tuytelaars, T. (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/cvpr42600.2020.01447 · Cited by 30
Self-citation
Continuous Learning Method of Radar HRRP Based on CVAE-GAN
Li, X., Ouyang, W., Pan, M., Lv, S., & Ma, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3268219 · Cited by 29
Lifelong Object DetectionPreprint
Zhou, W., Chang, S., Sosa, N. E., Hamann, H. F., & Cox, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.01129 · Cited by 42
Imbalanced class incremental learning system: A task incremental diagnosis method for imbalanced industrial streaming data
Shi, M., Ding, C., Shen, C., Huang, W., & Zhu, Z. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102832 · Cited by 29
Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting
Kurmi, V. K., Patro, B. N., Subramanian, V. K., & Namboodiri, V. P. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00078 · Cited by 30
Ternary Feature Masks: zero-forgetting for task-incremental learning
Masana, M., Tuytelaars, T., & van de Weijer, J. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00396 · Cited by 44
Self-citation
Rehearsal-free Continual Language Learning via Efficient Parameter Isolation
Wang, Z., Liu, Y., Ji, T., Wang, X., Wu, Y., Jiang, C., Ye, C., Han, Z., Wang, L., Shao, X., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.612 · Cited by 35
End-to-End Incremental LearningPreprint
Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C., & Alahari, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09536 · Cited by 28
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
Zhang, Z., Gao, G., Jiao, J., Liu, C. H., & Wei, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00084 · Cited by 27
Continual learning in medical image analysis: A comprehensive review of recent advancements and future prospects
Kumari, P., Chauhan, J., Bozorgpour, A., Huang, B., Azad, R., & Merhof, D. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103730 · Cited by 46
Lifelong Spectral Clustering
Sun, G., Cong, Y., Wang, Q., Li, J., & Fu, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6045 · Cited by 26
RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object Detection
Yang, D., Zhou, Y., Shi, W., Wu, D., & Wang, W. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3472393 · Cited by 26
Hyperspectral Image Classification Based on Class-Incremental Learning with Knowledge Distillation
Xu, M., Zhao, Y., Yajun, L., & Ma, X. (2022) · Remote Sensing · doi:10.3390/rs14112556 · Cited by 26

Authors: Rannen, A., Aljundi, R., Blaschko, M. B., & Tuytelaars, T.
Affiliations: IMEC (BE)
ORCID: Blaschko, Matthew B.
Tuytelaars, Tinne
Venue: 2017 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2017-10
Pages: 1329-1337
DOI: 10.1109/iccv.2017.148
OpenAlex: W2605911906
Cited by: 324
References: 23
Citations by year: 2026: 10 · 2025: 23 · 2024: 58 · 2023: 53 · 2022: 47 · 2021: 46 · 2020: 41 · 2019: 23 · 2018: 22 · 2017: 1
FWCI: 10.85
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Lirias (submittedVersion, other-oa)
arXiv (Cornell University)
Funders: Fonds Wetenschappelijk Onderzoek
KU Leuven (FP7-MC-CIG 334380)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords: lifelong learning, catastrophic forgetting, autoencoder, feature preservation, image classification, vision system

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

Encoder Based Lifelong Learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks