Fetching the paper…
Reading the bibliography…
The continual learning (CL) paradigm aims to enable neural networks to learn tasks continually in a sequential fashion.
Continual learning via neural pruning
Golkar, S., Kagan, M., Cho, K., 2019 · 1903
Earlier work this paper cites.
Three scenarios for continual learning, in: Continual Learning Workshop NeurIPS
van de Ven, G.M., Tolias, A.S., 2018 · 1904
Earlier work this paper cites.
Sparse networks from scratch: Faster training without losing performance
Dettmers, T., Zettlemoyer, L., 2019 · 1907
Earlier work this paper cites.
Rigging the lottery: Making all tickets winners
Evci, U., Gale, T., Menick, J., Castro, P.S., Elsen, E., 2019 · 1911
Earlier work this paper cites.
The organization of behavior. volume 65
Hebb, D.O., Hebb, D., 1949 · 1949
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem, in: Psychology of learning and motivation. Elsevier. volume 24, pp. 109–165
McCloskey, M., Cohen, N.J., 1989 · 1989
Earlier work this paper cites.
Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks, in: Proceedings of the 13th annual cognitive science society conference, pp. 173–178
French, R.M., 1991 · 1991
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Y., 1998 · 1998
Earlier work this paper cites.
Topological insights in sparse neural networks
Liu, S., Van der Lee, T., Yaman, A., Atashgahi, Z., Ferraro, D., Sokar, G., Pechenizkiy, M., Mocanu, D.C., 2020 · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al., 2009 · 2009
Earlier work this paper cites.
icarl: Incremental classifier and representation learning, in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2001–2010
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H., 2017 · 2010
Earlier work this paper cites.
Distilling the knowledge in a neural network. nips deep learning workshop
Hinton, G., Vinyals, O., Dean, J., 2014 · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE international conference on computer vision, pp. 1026–1034
He, K., Zhang, X., Ren, S., Sun, J., 2015 · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C., 2015 · 2015
Earlier work this paper cites.
Deep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S., 2016 · 2016
Earlier work this paper cites.
Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R., 2016 · 2016
Cited alongside, same era.
Zagoruyko, S., Komodakis, N., 2016 · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L., 2017 · 2017
Cited alongside, same era.
Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., Pritzel, A., Wierstra, D., 2017 · 2017
Cited alongside, same era.
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 · 2017
Hsu, Y.C., Liu, Y.C., Ramasamy, A., Kira, Z., 2018 · 2018
Later among the works it cites.
Measuring catastrophic forgetting in neural networks, in: Thirty-second AAAI conference on artificial intelligence
Kemker, R., McClure, M., Abitino, A., Hayes, T.L., Kanan, C., 2018 · 2018
Later among the works it cites.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 67–82
Mallya, A., Davis, D., Lazebnik, S., 2018 · 2018
Later among the works it cites.
Packnet: Adding multiple tasks to a single network by iterative pruning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7765–7773
Mallya, A., Lazebnik, S., 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning without forgetting
Li, Z., Hoiem, D., 2017 · 2017
Cited alongside, same era.
Feature pyramid networks for object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S., 2017 · 2017
Cited alongside, same era.
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E., 2017 · 2017
Cited alongside, same era.
Core50: a new dataset and benchmark for continuous object recognition, in: Conference on Robot Learning, pp. 17–26
Lomonaco, V., Maltoni, D., 2017 · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning, in: Advances in neural information processing systems, pp. 6467–6476
Lopez-Paz, D., Ranzato, M., 2017 · 2017
Cited alongside, same era.
Continual learning with deep generative replay, in: Advances in Neural Information Processing Systems, pp. 2990–2999
Shin, H., Lee, J.K., Kim, J., Kim, J., 2017 · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., Vollgraf, R., 2017 · 2017
Cited alongside, same era.
Mocanu, D.C., Mocanu, E., Stone, P., Nguyen, P.H., Gibescu, M., Liotta, A., 2018 · 2018
Later among the works it cites.
Progress & compress: A scalable framework for continual learning, in: ICML
Schwarz, J., Czarnecki, W., Luketina, J., Grabska-Barwinska, A., Teh, Y.W., Pascanu, R., Hadsell, R., 2018 · 2018
Later among the works it cites.
Lifelong learning with dynamically expandable networks, in: International Conference on Learning Representations
Yoon, J., Yang, E., Lee, J., Hwang, S.J., 2018 · 2018
Later among the works it cites.
Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V., 2018 · 2018
Later among the works it cites.
Farquhar, S., Gal, Y., 2019 · 2019
Later among the works it cites.
Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers, in: International Conference on Learning Representations
Junjie, L., Zhe, X., Runbin, S., Cheung, R.C., So, H.K., 2019 · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of NAACL-HLT, pp. 4171–4186
Kenton, J.D.M.W.C., Toutanova, L.K., 2019 · 2019
Later among the works it cites.
Lca: Loss change allocation for neural network training, in: Advances in Neural Information Processing Systems, pp. 3619–3629
Lan, J., Liu, R., Zhou, H., Yosinski, J., 2019 · 2019
Later among the works it cites.
Continuous learning in single-incremental-task scenarios
Maltoni, D., Lomonaco, V., 2019 · 2019
Later among the works it cites.
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization, in: International Conference on Machine Learning, pp. 4646–4655
Mostafa, H., Wang, X., 2019 · 2019
Later among the works it cites.
Efficient continual learning in neural networks with embedding regularization
Pomponi, J., Scardapane, S., Lomonaco, V., Uncini, A., 2020 · 2020
Closest in time.