Built on

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
R. M. French, “Catastrophic forgetting in connectionist networks,” Trends in Cognitive Sciences , vol. 3, no. 4, pp. 128–135, 1999
1999
Earlier work this paper cites.
J. Fagot and R. G. Cook, “Evidence for large long-term memory capacities in baboons and pigeons and its implications for learning and the evolution of cognition,” Proceedings of the National Academy of Sciences , vol. 103, no. 46, pp. 17 564–17 567, 2006
2006
Earlier work this paper cites.
A. Krizhevsky, V. Nair, and G. Hinton, “Cifar-10 (canadian institute for advanced research),” 2009. [Online]. Available: http://www.cs.toronto.edu/~kriz/cifar.html
2009
Earlier work this paper cites.
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber, “Compete to compute,” in Advances in Neural Information Processing Systems 26 , C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 2310–2318. [Online]. Available: http://papers.nips.cc/paper/5059-compete-to-compute.pdf
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
Original
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531 , 2015
Original
2015
Earlier work this paper cites.
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell, “Progressive Neural Networks,” ArXiv e-prints , Jun. 2016
2016
Earlier work this paper cites.
Z. Li and D. Hoiem, “Learning without Forgetting,” ArXiv e-prints , Jun. 2016
2016
Earlier work this paper cites.
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” CoRR , vol. abs/1606.03498, 2016
Original
2016
Earlier work this paper cites.

Similar

C. V. Nguyen, Y. Li, T. D. Bui, and R. E. Turner, “Variational continual learning,” arXiv preprint arXiv:1710.10628 , 2017
Original
2017
Cited alongside, same era.
A. Seff, A. Beatson, D. Suo, and H. Liu, “Continual learning in generative adversarial nets,” arXiv preprint arXiv:1705.08395 , 2017
Original
2017
Cited alongside, same era.
H. Shin, J. K. Lee, J. Kim, and J. Kim, “Continual learning with deep generative replay,” in Advances in Neural Information Processing Systems , 2017, pp. 2990–2999
2017
Cited alongside, same era.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , p. 201611835, 2017
2017
Cited alongside, same era.

Then

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875 , 2017
Original
2017
Later among the works it cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in Neural Information Processing Systems , 2017, pp. 5767–5777
2017
Later among the works it cites.
J. Schwarz, J. Luketina, W. M. Czarnecki, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, and R. Hadsell, “Progress & compress: A scalable framework for continual learning,” arXiv preprint arXiv:1805.06370 , 2018
Original
2018
Closest in time.
T. Lesort, J.-F. Goudou, and D. Filliat, “Training discriminative models to evaluate generative ones,” arXiv preprint arXiv:1806.10840 , 2018
Original
2018
Closest in time.
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, Z. Zhang, and Y. Fu, “Incremental classifier learning with generative adversarial networks,” arXiv preprint arXiv:1802.00853 , 2018

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2018

Generative Models from the perspective of Continual Learning

Lesort, Timothée, Caselles-Dupré, Hugo, Garcia-Ortiz, Michael et al.

Understand

Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks.

We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning.
We used two quantitative metrics to estimate the generation quality and memory ability.
We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).

Built on

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
R. M. French, “Catastrophic forgetting in connectionist networks,” Trends in Cognitive Sciences , vol. 3, no. 4, pp. 128–135, 1999
1999
Earlier work this paper cites.
J. Fagot and R. G. Cook, “Evidence for large long-term memory capacities in baboons and pigeons and its implications for learning and the evolution of cognition,” Proceedings of the National Academy of Sciences , vol. 103, no. 46, pp. 17 564–17 567, 2006
2006
Earlier work this paper cites.
A. Krizhevsky, V. Nair, and G. Hinton, “Cifar-10 (canadian institute for advanced research),” 2009. [Online]. Available: http://www.cs.toronto.edu/~kriz/cifar.html
2009
Earlier work this paper cites.
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber, “Compete to compute,” in Advances in Neural Information Processing Systems 26 , C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 2310–2318. [Online]. Available: http://papers.nips.cc/paper/5059-compete-to-compute.pdf
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
Original
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531 , 2015
Original
2015
Earlier work this paper cites.
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell, “Progressive Neural Networks,” ArXiv e-prints , Jun. 2016
2016
Earlier work this paper cites.
Z. Li and D. Hoiem, “Learning without Forgetting,” ArXiv e-prints , Jun. 2016
2016
Earlier work this paper cites.
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” CoRR , vol. abs/1606.03498, 2016
Original
2016
Earlier work this paper cites.

Similar

C. V. Nguyen, Y. Li, T. D. Bui, and R. E. Turner, “Variational continual learning,” arXiv preprint arXiv:1710.10628 , 2017
Original
2017
Cited alongside, same era.
A. Seff, A. Beatson, D. Suo, and H. Liu, “Continual learning in generative adversarial nets,” arXiv preprint arXiv:1705.08395 , 2017
Original
2017
Cited alongside, same era.
H. Shin, J. K. Lee, J. Kim, and J. Kim, “Continual learning with deep generative replay,” in Advances in Neural Information Processing Systems , 2017, pp. 2990–2999
2017
Cited alongside, same era.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , p. 201611835, 2017
2017
Cited alongside, same era.

Then

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875 , 2017
Original
2017
Later among the works it cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in Neural Information Processing Systems , 2017, pp. 5767–5777
2017
Later among the works it cites.
J. Schwarz, J. Luketina, W. M. Czarnecki, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, and R. Hadsell, “Progress & compress: A scalable framework for continual learning,” arXiv preprint arXiv:1805.06370 , 2018
Original
2018
Closest in time.
T. Lesort, J.-F. Goudou, and D. Filliat, “Training discriminative models to evaluate generative ones,” arXiv preprint arXiv:1806.10840 , 2018
Original
2018
Closest in time.
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, Z. Zhang, and Y. Fu, “Incremental classifier learning with generative adversarial networks,” arXiv preprint arXiv:1802.00853 , 2018

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,” 2017

2017

Cited alongside, same era.

H. Xiao, K. Rasul, and R. Vollgraf. (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms

2017

Cited alongside, same era.

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems , 2017, pp. 6626–6637

2017

Cited alongside, same era.

Z. Li and D. Hoiem, “Learning without forgetting,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017

2017

Cited alongside, same era.

C. Fernando, D. Banarse, C. Blundell, Y. Zwols, D. Ha, A. A. Rusu, A. Pritzel, and D. Wierstra, “Pathnet: Evolution channels gradient descent in super neural networks,” CoRR , vol. abs/1701.08734, 2017. [Online]. Available: http://arxiv.org/abs/1701.08734

Original

2017

Cited alongside, same era.

R. Venkatesan, H. Venkateswara, S. Panchanathan, and B. Li, “A strategy for an uncompromising incremental learner,” arXiv preprint arXiv:1705.00744 , 2017

Original

2017

Cited alongside, same era.

J. Ramapuram, M. Gregorova, and A. Kalousis, “Lifelong generative modeling,” arXiv preprint arXiv:1705.09847 , 2017

Original

2017

Cited alongside, same era.

C. Li, H. Liu, C. Chen, Y. Pu, L. Chen, R. Henao, and L. Carin, “Alice: Towards understanding adversarial learning for joint distribution matching,” in Advances in Neural Information Processing Systems , 2017, pp. 5495–5503

2017

Cited alongside, same era.

Original

2018

Closest in time.

A. Achille, T. Eccles, L. Matthey, C. P. Burgess, N. Watters, A. Lerchner, and I. Higgins, “Life-long disentangled representation learning with cross-domain latent homologies,” arXiv preprint arXiv:1808.06508 , 2018

Original

2018

Closest in time.

H. Shah, K. Javed, and F. Shafait, “Distillation techniques for pseudo-rehearsal based incremental learning,” arXiv preprint arXiv:1807.02799 , 2018

Original

2018

Closest in time.

C. Wu, L. Herranz, X. Liu, Y. Wang, J. van de Weijer, and B. Raducanu, “Memory replay gans: learning to generate images from new categories without forgetting,” arXiv preprint arXiv:1809.02058 , 2018

Original

2018

Closest in time.

S. Barratt and R. Sharma, “A note on the inception score,” arXiv preprint arXiv:1801.01973 , 2018

Original

2018

Closest in time.

A. Borji, “Pros and cons of gan evaluation measures,” arXiv preprint arXiv:1802.03446 , 2018

Original

2018

Closest in time.

X. He and H. Jaeger, “Overcoming catastrophic interference using conceptor-aided backpropagation,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=B1al7jg0b

2018

Closest in time.