Research

In the last few years we have witnessed a renewed and steadily growing interest in the ability to learn continuously from high-dimensional data. In this page, we will keep track of recent Continual/Lifelong Learning developments in the research community.

Review Papers and Books

• Continual Lifelong Learning with Neural Networks: A Review by German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter. Neural Networks, 113:54–71, 2019.

• Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks by Andrea Soltoggio, Kenneth Stanley, and Sebastian Risi. Neural Networks, 108:48–67, 2018.

• Lifelong Machine Learning (2nd Edition) by Zhiyuan Chen and Bing Liu. Morgan & Claypool Publishers, August 2018.

Empirical Studies and Comparisons

• “Continuous Learning in Single-Incremental-Task Scenarios” by Davide Maltoni and Vincenzo Lomonaco. Neural Networks, 116:56–73, 2019.

• “CORe50: a new Dataset and Benchmark for Continuous Object Recognition” by Vincenzo Lomonaco and Davide Maltoni. Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:17-26, 2017.

• “Measuring catastrophic forgetting in neural networks” by Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L. Hayes, and Christopher Kanan. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2018.

• “Generative Models from the perspective of Continual Learning” by Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian, David Filliat. International Joint Conference on Neural Networks (IJCNN-19), 2019.

• “An empirical investigation of catastrophic forgetting in gradient-based neural networks” by Ian J. Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, Yoshua Bengio. arXiv:1312.6211, 2013.

Metrics and Evaluations

• “New Metrics and Experimental Paradigms for Continual Learning” by Tyler L. Hayes, Ronald Kemker, Nathan D. Cahill, Christopher Kanan. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2031-2034, 2018.

• “Don’t forget, there is more than forgetting: new metrics for Continual Learning” by Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, Davide Maltoni. Advances in Neural Information Processing Systems, Continual Learning Workshop, 2018.

• “Towards Robust Evaluations of Continual Learning” by Sebastian Farquhar, Yarin Gal. arXiv:1805.09733, 2018.

• “Measuring Catastrophic Forgetting in Neural Networks” by Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, Christopher Kanan. Thirty-second AAAI conference on artificial intelligence, 2018.

Conference Workshops

• Cosyne 2019 Continual Learning

• ICML 2019 Workshop on Multi-Task and Lifelong Reinforcement Learning

• ICML 2019 Adaptive and Multitask Learning: Algorithms & Systems

• ICML 2018 Lifelong RL workshop

• NeurIPS 2018 Workshop on Meta-Learning

• NeurIPS 2018 Workshop on Continual Learning

Research Programs

In this section we keep track of all the current and past projects on Lifelong/Continual Learning.:

• DARPA Lifelong Learning Machines (L2M) program

• European H2020 DREAM project

Community Selected Papers

In this section we highlight some papers the Continual AI community value as must-read:

• Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. “Continual Learning with Deep Generative Replay”. Advances in Neural Information Processing Systems, 2017.

• Xu He and Herbert Jaeger. “Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation”. International Conference on Learning Representations, 2018.

• Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang. “Lifelong Learning with Dynamically Expandable Networks”. International Conference on Learning Representations, 2018.

• Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner. “Variational Continual Learning”. International Conference on Learning Representations, 2018.

• Vincenzo Lomonaco and Davide Maltoni. “CORe50: a new Dataset and Benchmark for Continuous Object Recognition”. Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:17-26, 2017.

• James Kirkpatrick & All. “Overcoming catastrophic forgetting in neural networks”. Proceedings of the National Academy of Sciences, 2017, 201611835.

• Li Zhizhong and Derek Hoiem. “Learning without forgetting”. European Conference on Computer Vision. Springer International Publishing, 2016.

• Lopez-Paz David and Marc’Aurelio Ranzato. “Gradient Episodic Memory for Continual Learning”. Advances in Neural Information Processing Systems, 2017.

• Rebuffi Sylvestre-Alvise, Alexander Kolesnikov and Christoph H. Lampert. “iCaRL: Incremental classifier and representation learning.” arXiv preprint arXiv:1611.07725, 2016.

• Zenke, Friedemann, Ben Poole, and Surya Ganguli. “Continual learning through synaptic intelligence”. International Conference on Machine Learning, 2017.

• Rusu Andrei et al. “Progressive neural networks.” arXiv preprint arXiv:1606.04671, 2016.

• German I. Parisi, Jun Tani, Cornelius Weber and Stefan Wermter. “Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization”. Frontiers in Neurorobotics, 28, 2018.

Dissertations and Theses

• “Continual Learning with Deep Architectures” by Lomonaco, Vincenzo. Alma Mater Studiorum Università di Bologna, 2019.

• “Explanation-Based Neural Network Learning: A Lifelong Learning Approach” by Sebastian Thrun. Kluwer Academic Publishers, Boston, MA, 1996.

• “Continual Learning in Reinforcement Environments” by Mark Ring. The University of Texas at Austin, 1994.

Papers Database

Waiting for better AI tools for papers reccomendation the Continual AI community is mantaining a database of CL papers which we plan to realease soon. It would be very rich of metadata so that we can better navigate the incredible number of papers published each year (query example: give me the papers employing reharsal and evaluated on CORe50).

Please add your own paper below so that we can advertise it and insert in our CL database!