Resources collection for the hot research topic of Continual Learning, a fundamental step stone to Artificial General Intelligence (AGI).
Papers:
- 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.
- Davide Maltoni and Vincenzo Lomonaco. “Continuous Learning in Single-Incremental-Task Scenarios.” arXiv preprint arXiv:1606.04671, 2018.
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