All the course material in one page!
Classic readings on catastrophic forgetting
Catastrophic Forgetting; Catastrophic Interference; Stability; Plasticity; Rehearsal. by and Anthony Robins. Connection Science, 123--146, 1995.
Using Semi-Distributed Representations to Overcome Catastrophic Forgetting in Connectionist Networks by and Robert French. In Proceedings of the 13th Annual Cognitive Science Society Conference, 173--178, 1991. [sparsity]
Check out additional material for popular reviews and surveys on continual learning.
Lifelong Machine Learning, Second Edition. by Zhiyuan Chen and Bing Liu. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2018.
Classic references on Catastrophic Forgetting provided above.
Does Continual Learning = Catastrophic Forgetting?, by A. Thai, S. Stojanov, I. Rehg, and J. M. Rehg, arXiv, 2021.
An Empirical Study of Example Forgetting during Deep Neural Network Learning, by M. Toneva, A. Sordoni, R. T. des Combes, A. Trischler, Y. Bengio, and G. J. Gordon, ICLR, 2019.
Compete to Compute, by R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber, NIPS, 2013 (Permuted MNIST task).
Three scenarios for continual learning, by G. M. van de Ven and A. S. Tolias, Continual Learning workshop at NeurIPS, 2018. task/domain/class incremental learning
Continuous Learning in Single-Incremental-Task Scenarios, by D. Maltoni and V. Lomonaco, Neural Networks, vol. 116, pp. 56–73, 2019. New Classes (NC), New Instances (NI), New Instances and Classes (NIC) + Single Incremental (SIT) /Multi (MT) /Multi Incremental (MIT) Task
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams, by M. De Lange and T. Tuytelaars, ICCV, 2021. Data-incremental and comparisons with other CL scenarios
Survey presenting CL scenarios
Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges by Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat and Natalia Díaz-Rodr\ǵuez. Information Fusion, 52--68, 2020. Section 3, in particular.
CORe50: a New Dataset and Benchmark for Continuous Object Recognition, by V. Lomonaco and D. Maltoni, Proceedings of the 1st Annual Conference on Robot Learning, vol. 78, pp. 17–26, 2017.
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning, by Q. She et al. ICRA, 2020.
Incremental Object Learning From Contiguous Views, by S. Stojanov et al., CVPR, 2019. CRIB benchmark
Stream-51: Streaming Classification and Novelty Detection From Videos, by R. Roady, T. L. Hayes, H. Vaidya, and C. Kanan, CVPR 2019.
Efficient Lifelong Learning with A-GEM, by A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny, ICLR, 2019. Evaluation protocol with "split by experiences".
Gradient Episodic Memory for Continual Learning, by D. Lopez-Paz and M. Ranzato, NIPS, 2017. popular formalization of ACC, BWT, FWT.
CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability, by M. Mundt, S. Lang, Q. Delfosse, and K. Kersting, arXiv, 2021.
Don’t forget, there is more than forgetting: new metrics for Continual Learning, by N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, and D. Maltoni, arXiv, 2018. definition of additional metrics
GDumb: A Simple Approach that Questions Our Progress in Continual Learning, by A. Prabhu, P. H. S. Torr, and P. K. Dokania, ECCV, 2020.
Latent Replay for Real-Time Continual Learning, by Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni, IROS, 2020.
Continual Learning with Deep Generative Replay, by H. Shin, J. K. Lee, J. Kim, and J. Kim, NeurIPS, 2017.
Brain-inspired replay for continual learning with artificial neural networks, by G. M. van de Ven, H. T. Siegelmann, and A. S. Tolias, Nature Communications, 2020
L1, L2, Dropout
An Empirical Investigation of Catatrophic Forgetting in Gradient-Based Neural Networks, by Goodfellow et al, 2015.
Understanding the Role of Training Regimes in Continual Learning, by Mirzadeh et al., NeurIPS, 2020.
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches, by Lomonaco et al, CLVision Workshop at CVPR 2020. CWR*
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning, by Mallya et al., CVPR, 2018.
Continual Learning at the Edge: Real-Time Training on Smartphone Devices, by L. Pellegrini et al., ESANN, 2021.
Embracing Change: Continual Learning in Deep Neural Networks, by Hadsell et al., Trends in Cognitive Science, 2020. Continual meta learning - Meta continual learning
Towards Continual Reinforcement Learning: A Review and Perspectives, by Khetarpal et al, arXiv, 2020.
Distributed Continual Learning Ex-Model: Continual Learning from a Stream of Trained Models, by Carta et al., arXiv, 2021.
Continual Sequence Learning Continual learning for recurrent neural networks: An empirical evaluation, by Cossu et al, Neural Networks, vol. 143, pp. 607–627, 2021. Continual Learning with Echo State Networks, by Cossu et al., ESANN, 2021.
Avalanche: an End-to-End Library for Continual Learning, the software library based on PyTorch used for the coding session of this course.
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