Gradient episodic memory for continual learning

Advances in Neural Information Processing Systems

Volumn 2017-December, Issue , 2017, Pages 6468-6477

  Lopez Paz, David ^a   Ranzato, Marc'Aurelio ^a  

^a FACEBOOK AI RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUAL LEARNING; EPISODIC MEMORY; STATE OF THE ART; TEST ACCURACY; TRANSFER OF KNOWLEDGE;

KNOWLEDGE MANAGEMENT;

EID: 85047021111     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (53)

1. R. Aljundi, P. Chakravarty, and T. Tuytelaars. Expert gate: Lifelong learning with a network of experts. CVPR, 2016.
2. M.-F. Balcan, A. Blum, and S. Vempola. Efficient representations for lifelong learning and autoencoding. COLT, 2015.
3. M. Baroni, A. Joulin, A. Jabri, G. Kruszewski, A. Lazaridou, K. Simonic, and T. Mikolov. CommAI: Evaluating the first steps towards a useful general AI. arXiv, 2017.
4. J. Baxter. A model of inductive bias learning. JAIR, 2000.
5. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman Vaughan. A theory of learning from different domains. Machine Learning Journal, 2010.
6. Y. Bengio, J. Louradour, R. Collobert, and J. Weston. Curriculum learning. ICML, 2009.
7. L. Bertinetto, J. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. Learning feed-forward one-shot learners. NIPS, 2016.
8. A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. Hruschka, and T. M. Mitchell. Toward an architecture for never-ending language learning. AAAI, 2010.
9. R. Caruana. Multitask learning. In Learning to learn. Springer, 1998.
10. L. Denoyer and P. Gallinari. Deep sequential neural networks. EWRL, 2015.
11. W. S. Dorn. Duality in quadratic programming. Quarterly of Applied Mathematics, 1960.
12. D. Eigen, I. Sutskever, and M. Ranzato. Learning factored representations in a deep mixture of experts. ICLR, 2014.
13. L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. ICCV, 2003.
14. 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. arXiv, 2017.
15. R. M. French. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 1999.
16. I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, and Y. Bengio. An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks. arXiv, 2013.
17. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv, 2015.
18. G. Hinton, O. Vinyals, and J. Dean. Distilling the knowledge in a neural network. arXiv, 2015.
19. H. Jung, J. Ju, M. Jung, and J. Kim. Less-forgetting Learning in Deep Neural Networks. arXiv, 2016.
20. 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. PNAS, 2017.
21. A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, Technical report, University of Toronto, 2009.
22. C. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. CVPR, 2009.
23. Y. LeCun, C. Cortes, and C. J. Burges. The MNIST database of handwritten digits, 1998. URL http://yann.lecun.com/exdb/mnist/.
24. Z. Li and D. Hoiem. Learning without forgetting. ECCV, 2016.
25. M. Lucic, M. Faulkner, A. Krause, and D. Feldman. Training Mixture Models at Scale via Coresets. arXiv, 2017.
26. J. L. McClelland, B. L. McNaughton, and R. C. O'reilly. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological review, 1995.
27. M. McCloskey and N. J. Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of learning and motivation, 1989.
28. T. Mikolov, A. Joulin, and M. Baroni. A roadmap towards machine intelligence. arXiv, 2015.
29. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. CVPR, 2014.
30. M. Palatucci, D. A. Pomerleau, G. E. Hinton, and T. Mitchell. Zero-shot learning with semantic output codes. NIPS, 2009.
31. S. J. Pan and Q. Yang. A survey on transfer learning. TKDE, 2010.
32. A. Pentina and R. Urner. Lifelong learning with weighted majority votes. NIPS, 2016.
33. A. Pentina, V. Sharmanska, and C. H. Lampert. Curriculum learning of multiple tasks. CVPR, 2015.
34. J. Peters, P. Bühlmann, and N. Meinshausen. Causal inference by using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society, 2016.
35. A. Rannen Triki, R. Aljundi, M. B. Blaschko, and T. Tuytelaars. Encoder Based Lifelong Learning. arXiv, 2017.
36. R. Ratcliff. Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychological review, 1990.
37. S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert. iCaRL: Incremental classifier and representation learning. CVPR, 2017.
38. M. B. Ring. Continual Learning in Reinforcement Environments. PhD thesis, University of Texas at Austin, Austin, Texas 78712, 1994.
39. M. B. Ring. CHILD: A first step towards continual learning. Machine Learning, 1997.
40. A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell. Progressive neural networks. NIPS, 2016.
41. P. Ruvolo and E. Eaton. ELLA: An Efficient Lifelong Learning Algorithm. ICML, 2013.
42. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. One-shot learning with memory-augmented neural networks. arXiv, 2016.
43. T. Schaul, D. Horgan, K. Gregor, and D. Silver. Universal value function approximators. ICML, 2015.
44. B. Scholkopf and A. J. Smola. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2001.
45. B. Schölkopf, D. Janzing, and D. Lopez-Paz. Causal and statistical learning. In Learning Theory and Approximation. Oberwolfach Research Institute for Mathematics, 2016.
46. R. S. Sutton, J. Modayil, M. Delp, T. Degris, P. M. Pilarski, A. White, and D. Precup. Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. The 10th International Conference on Autonomous Agents and Multiagent Systems, 2011.
47. S. Thrun. A lifelong learning perspective for mobile robot control. Proceedings of the IEEE/RSJ/GI Conference on Intelligent Robots and Systems, 1994.
48. S. Thrun. Is learning the n-th thing any easier than learning the first? NIPS, 1996.
49. S. Thrun. Lifelong learning algorithms. In Learning to learn. Springer, 1998.
50. S. Thrun and L. Pratt. Learning to learn. Springer Science & Business Media, 2012.
51. V. Vapnik. Statistical learning theory. Wiley New York, 1998.
52. O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra. Matching networks for one shot learning. NIPS, 2016.
53. F. Zenke, B. Poole, and S. Ganguli. Improved multitask learning through synaptic intelligence. arXiv, 2017.