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Continual learning from a sequential stream of data is a crucial challenge for machine learning research.
Carroll, J.: Allocation of a Sample Between States. Australian Bureau of Census and Statistics (1970)
1970
Earlier work this paper cites.
Fellegi, I.P.: Should the Census Counts Be Adjusted for Allocation Purposes?-Equity Considerations. Current Topics in Survey Sampling pp. 47–76 (1981)
1981
Earlier work this paper cites.
Vitter, J.S.: Random sampling with a reservoir. ACM Transactions on Mathematical Software (TOMS) 11
1985
Earlier work this paper cites.
Bankier, M.: Power Allocations: Determining Sample Sizes for Subnational Areas. The American Statician (1988)
1988
Earlier work this paper cites.
McCloskey, M., Cohen, N.J.: Catastrophic interference in conncectionist networks. Psychology of learning and motivation 24
1989
Earlier work this paper cites.
Ratcliff, R.: Conncectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Pscyhological review 97
1990
Earlier work this paper cites.
Caruaca, R.: Multitask learning. Machine Learning 28
1997
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient based learning applied to document recognition. In: IEEE (1998)
1998
Earlier work this paper cites.
French, R.: Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences 3
1999
Earlier work this paper cites.
Reed, W.J.: The pareto, zipf and other power laws. Economic letters 74
2001
Earlier work this paper cites.
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16
2002
Earlier work this paper cites.
Japkowicz, N., Stephen, S.: The class imbalance problem: A systematic study. Intelligent Data Analysis 6
2002
Earlier work this paper cites.
Chawla, N., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smoteboost: improving prediction of the minority class in boosting. In: Knowledge discovery in databases: PKDD (2003)
2003
Earlier work this paper cites.
Drummond, C., Holte, R.C., et al.: C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets (2003)
2003
Earlier work this paper cites.
Batista, G., Prati, R., Monard, M.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor 6
2004
Earlier work this paper cites.
Chen, C., Liaw, A., Breiman, L., et al.: Using random forest to learn imbalanced data. University of California, Berkeley 110
2004
Earlier work this paper cites.
Greensmith, E., Bartlett, P.L., Baxter, J.: Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research 5
2004
Earlier work this paper cites.
Newman, M.: Power laws, pareto distributions and zipf’s law. Contemporary Physics 46
2005
Earlier work this paper cites.
Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on knowledge and data engineering 18
2005
Earlier work this paper cites.
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 9
2008
Earlier work this paper cites.
van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9
2008
Earlier work this paper cites.
Makadia, A., Pavlovic, V., Kumar, S.: A new baseline for image annotation. In: ECCV (2008)
2008
Earlier work this paper cites.
Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: Svms modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39
2008
Earlier work this paper cites.
Chua, T.S., Tang, J., Hong, R., Li, H., luo, Z., Zheng, Y.: Nus-wide: a real-world web image database from national university of singapore. In: ACM (2009)
2009
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
2009
Earlier work this paper cites.
Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In: ICCV (2009)
2009
Earlier work this paper cites.
Maciejewski, T., Stefanowski, J.: Local neighbourhood extension of smote for mining imbalanced data. In: CIDM (2011)
2011
Earlier work this paper cites.
Wang, Y.N.T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NeurIPS (2011)
2011
Earlier work this paper cites.
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, L.C.: Microsoft COCO: Common objects in Context. In: ECCV (2014)
2014
Earlier work this paper cites.
Zhu, X., Anguelov, D., Ramanan, D.: Capturing long-tail distributions of object subcategories. In: CVPR (2014)
2014
Earlier work this paper cites.
Bengio, S.: Sharing representations for long tail computer vision problems. In: ICMI (2015)
2015
Earlier work this paper cites.
Kingma, D., Ba, J.: Adam: A Method for Stochastic Optimization. In: ICLR (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Huang, C., Li, Y., Change Loy, C., Tang, X.: Learning deep representation for imbalanced classification. In: CVPR (2016)
2016
Cited alongside, same era.
Krawczyk, B.: Learning from imbalanced data:open challenges and future directions. Progress in Artificial Intelligence 5
2016
Cited alongside, same era.
Lee, H., Park, M., Kim, J.: Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning. In: ICIP (2016)
2016
Cited alongside, same era.
Li, Z., Hoiem, D.: Learning without forgetting. In: ECCV (2016)
2016
Cited alongside, same era.
Ouyang, W., Wang, X., Zhang, C., Yang, X.: Factors in finetuning deep model for object detection with long-tail distribution. In: CVPR (2016)
2016
Cited alongside, same era.
Parisi, G.I., Tani, J., Weber, C., Wermter, S.: Lifelong learning of spatiotemporal representations with dual-memory recurrent self-organization. Frontiers in Neurorobotics 12
2018
Later among the works it cites.
2018
Later among the works it cites.
Wu, C., Herranz, L., Liu, X., Wang, Y., van de Weijer, J., Raducanu, B.: Memory replay gans: Learning to generate new categories without forgetting. In: NeurIPS (2018)
2018
Later among the works it cites.
Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. In: ICLR (2018)
2018
Later among the works it cites.
Aljundi, R.: Continual Learning in Neural Networks. Ph.D. thesis, Department of Electrical Engineering, KU Leuven (2019)
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2016
Cited alongside, same era.
Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: ECCV (2016)
2016
Cited alongside, same era.
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: A Unified Framework for Multi-label Image Classification. In: CVPR (2016)
2016
Cited alongside, same era.
Dong, Q., Gong, S., Zhu, X.: Class rectification hard mining for imbalanced deep learning. In: ICCV (2017)
2017
Cited alongside, same era.
Draelos, T.J., Miner, N.E., Lamb, C.C., Vineyard, C.M., Carlson, K.D., James, C.D., Aimone, J.B.: Neurogenesis deep learning. In: IJCNN (2017)
2017
Cited alongside, same era.
Finn, C., Abbeel, P., Sergey, L.: Model-agnositc meta-learning for fast adaptation of deep networks. In: ICML (2017)
2017
Cited alongside, same era.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., Hadsell, R.: Overcoming catastrophic forgetting in neural networks. In: Proceedings of the National Academy of Sciences (2017)
2017
Cited alongside, same era.
2019
Later among the works it cites.
Aljundi, R., Marcus, R., Tuytelaars, T.: Selfless sequential learning. In: ICLR (2019)
2019
Later among the works it cites.
Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Gradient based sample selection for online continual learning. In: NeurIPS (2019)
2019
Later among the works it cites.
Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with a-gem. In: ICLR (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Cui, Y., Jia, M., Lin, T., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR (2019)
2019
Later among the works it cites.
d’Autume, C., Ruder, S., Kong, L., Yogatama, D.: Episodic memory in lifelong language learning. In: NeurIPS (2019)
2019
Later among the works it cites.
Dong, Q., Gong, S., Zhu, X.: Imbalanced deep learning by minority class incremental rectification. TPAMI 41
2019
Later among the works it cites.
2019
Later among the works it cites.
Guo, H., Zheng, K., Fan, X., Yu, H., Wang, S.: Visual Attention Consistency under Image Transforms for Multi-Label Image Classification. In: CVPR (2019)
2019
Later among the works it cites.
Hayes, T.L., Cahill, N.D., Kanan, C.: Memory efficient experience replay for streaming learning. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)
2019
Later among the works it cites.
Lesort, T., Caselles-Dupré, H., Garcia-Ortiz, M., Stoian, A., Filliat, D.: Generative models from the perspective of continual learning. In: IJCNN (2019)
2019
Later among the works it cites.
Lesort, T., Gepperth, A., Stoian, A., Filliat, D.: Marginal replay vs conditional replay for continual learning. In: IJCANN (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: CVPR (2019)
2019
Later among the works it cites.
Maltoni, D., Lomonaco, V.: Continuous learning in single-incremental-task scenarios. Elsevier Neural Networks Journal 116
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., Tesauro, G.: Learning to learn without forgetting by maximizing transfer and minimizing interference. In: ICLR (2019)
2019
Later among the works it cites.
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T.P., Wayne, G.: Experience replay for continual learning. In: NeurIPS (2019)
2019
Later among the works it cites.
van de Ven, G.M., Andreas, S.T.: Three scenarios for continual learning. In: NeurIPS Continual Learning workshop (2019)
2019
Later among the works it cites.
Wei, Z.M.C.X.S., Wang, P., Guo, Y.: Multi-Label Image Recognition with Graph Convolutional Networks. In: CVPR (2019)
2019
Later among the works it cites.
Xu, M., Quiroz, M., Kohn, R., Sisson, S.A.: Variance reduction properties of the reparameterization trick. In: AISTATS (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Feature transfer learning for deep face recognition with under-represented data. In: CVPR (2019)
2019
Later among the works it cites.
Zhang, N., Deng, S., Sun, Z., Wang, G., Chen, X., Zhang, W., Chen, H.: Long-tail relation extraction via knowledge graph embeddings and. In: NAACL (2019)
2019
Later among the works it cites.
Lee, S., Ha, J., Zhang, D., Kim, G.: A neural dirichlet process mixture model for task-free continual learning. In: ICLR (2020)
2020
Closest in time.
Li, Y., Zhao, L., Church, K., Elhoseiny, M.: Compositional continual language learning. In: ICLR (2020)
2020
Closest in time.