Fetching the paper…
Reading the bibliography…
In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework.
Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Li, X., Zhou, Y., Wu, T., Socher, R., and Xiong, C · 1904
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J · 1989
Earlier work this paper cites.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Ratcliff, R · 1990
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, A · 1995
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
Earlier work this paper cites.
Laplace propagation
Smola, A. J., Vishwanathan, V., and Eskin, E · 2003
Earlier work this paper cites.
A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F · 2006
Earlier work this paper cites.
Stochastic gradient vb and the variational auto-encoder
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Online learning with random representations
Sutton, R. S., Whitehead, S. D., et al · 2014
Earlier work this paper cites.
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
Earlier work this paper cites.
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Overcoming catastrophic forgetting by incremental moment matching
Lee, S., Kim, J., Ha, J., and Zhang, B · 2017
Cited alongside, same era.
Learning without forgetting
Li, Z. and Hoiem, D · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Supportnet: solving catastrophic forgetting in class incremental learning with support data
Li, Y., Li, Z., Ding, L., Pan, Y., Huang, C., Hu, Y., Chen, W., and Gao, X · 2018
Later among the works it cites.
Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2018
Later among the works it cites.
Lifelong learning with dynamically expandable networks
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
Later among the works it cites.
Exponential family estimation via adversarial dynamics embedding
Dai, B., Liu, Z., Dai, H., He, N., Gretton, A., Song, L., and Schuurmans, D · 2019
Closest in time.
Implicit generation and generalization in energy-based models
Du, Y. and Mordatch, I · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The eu general data protection regulation (gdpr)
Voigt, P. and Von dem Bussche, A · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Cited alongside, same era.
End-to-end incremental learning
Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C., and Alahari, K · 2018
Cited alongside, same era.
Lifelong learning via progressive distillation and retrospection
Hou, S., Pan, X., Change Loy, C., Wang, Z., and Lin, D · 2018
Cited alongside, same era.
Revisiting distillation and incremental classifier learning
Javed, K. and Shafait, F · 2018
Cited alongside, same era.
A unifying bayesian view of continual learning
Farquhar, S. and Gal, Y · 2019
Closest in time.
Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., Norouzi, M., and Swersky, K · 2019
Closest in time.
Improving and understanding variational continual learning
Swaroop, S., Nguyen, C. V., Bui, T. D., and Turner, R. E · 2019
Closest in time.
Large scale incremental learning
Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., and Fu, Y · 2019
Closest in time.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Closest in time.
Gdumb: A simple approach that questions our progress in continual learning
Prabhu, A., Torr, P. H., and Dokania, P. K · 2020
Closest in time.
itaml: An incremental task-agnostic meta-learning approach
Rajasegaran, J., Khan, S., Hayat, M., Khan, F. S., and Shah, M · 2020
Closest in time.