floodsung/Lifelong-Learning-Paper-List

Lifelong Learning / Continual Learning

[1] Parisi, German I., et al. Continual Lifelong Learning with Neural Networks: A Review. arXiv preprint arXiv:1802.07569 (2018). link

[2] Gepperth, Alexander, and Cem Karaoguz. A bio-inspired incremental learning architecture for applied perceptual problems. Cognitive Computation 8.5 (2016): 924-934. link

[3] Lüders, Benno, Mikkel Schläger, and Sebastian Risi. Continual learning through evolvable neural turing machines. NIPS 2016 Workshop on Continual Learning and Deep Networks (CLDL 2016). 2016. link

[4] Shin, Hanul, et al. Continual learning with deep generative replay. Advances in Neural Information Processing Systems. 2017. link

[5] Maltoni, Davide, and Vincenzo Lomonaco. Continuous Learning in Single-Incremental-Task Scenarios. arXiv preprint arXiv:1806.08568 (2018). link

[6] Lomonaco, Vincenzo, and Davide Maltoni. Core50: a new dataset and benchmark for continuous object recognition. arXiv preprint arXiv:1705.03550 (2017). link

[7] Kamra, Nitin, Umang Gupta, and Yan Liu. Deep Generative Dual Memory Network for Continual Learning. arXiv preprint arXiv:1710.10368 (2017). link

[8] Triki, Amal Rannen, et al. Encoder based lifelong learning. arXiv preprint arXiv:1704.01920 (2017). link

[9] Aljundi, Rahaf, Punarjay Chakravarty, and Tinne Tuytelaars. Expert Gate: Lifelong Learning with a Network of Experts. CVPR. 2017. link

[10] Rebuffi, Sylvestre-Alvise, et al. icarl: Incremental classifier and representation learning. Proc. CVPR. 2017. link

[11] Castro, Francisco M., et al. End-to-end incremental learning. ECCV 2018-European Conference on Computer Vision. 2018. link

[12] Aljundi, Rahaf, et al. Memory Aware Synapses: Learning what (not) to forget. arXiv preprint arXiv:1711.09601 (2017). link

[13] Kemker, Ronald, and Christopher Kanan. Fearnet: Brain-inspired model for incremental learning. arXiv preprint arXiv:1711.10563 (2017). link

[14] Li, Zhizhong, and Derek Hoiem. Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017). link

[15] Parisi, German I., et al. Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization. arXiv preprint arXiv:1805.10966 (2018). link

[16] Vuorio, Risto, et al. Meta Continual Learning. arXiv preprint arXiv:1806.06928 (2018). link

[17] Kirkpatrick, James, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences (2017): 201611835. link

[18] Aljundi, Rahaf, Marcus Rohrbach, and Tinne Tuytelaars. Selfless Sequential Learning. arXiv preprint arXiv:1806.05421 (2018). link

[19] Nguyen, Cuong V., et al. Variational continual learning. arXiv preprint arXiv:1710.10628 (2017). link

[20] Hou, Saihui, et al. Lifelong learning via progressive distillation and retrospection. European Conference on Computer Vision. Springer, Cham, 2018. link

[21] Zenke, Friedemann, Ben Poole, and Surya Ganguli. Continual learning through synaptic intelligence. arXiv preprint arXiv:1703.04200 (2017). link

[22] Yoon, Jaehong, et al. "Lifelong Learning with Dynamically Expandable Networks." ICLR(2018). link

[23] Ritter, Hippolyt, Aleksandar Botev, and David Barber. "Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting." arXiv preprint arXiv:1805.07810 (2018). link

[24] Chaudhry, Arslan, et al. "Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence." arXiv preprint arXiv:1801.10112 (2018). link

[25] Lopez-Paz, David. Gradient episodic memory for continual learning. Advances in Neural Information Processing Systems. 2017. link

[26] Lee, Sang-Woo, et al. Overcoming catastrophic forgetting by incremental moment matching. Advances in Neural Information Processing Systems. 2017. link

[27] Schwarz, Jonathan, et al. Progress & Compress: A scalable framework for continual learning. arXiv preprint arXiv:1805.06370 (2018). link

[28] Rusu, Andrei A., et al. Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016). link