Course Materials
All the course material in one page!
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All the course material in one page!
Last updated
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Classic readings on catastrophic forgetting
by and Anthony Robins. Connection Science, 123--146, 1995.
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.
, Second Edition. by Zhiyuan Chen and Bing Liu. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2018.
Classic references on Catastrophic Forgetting provided above.
, by A. Thai, S. Stojanov, I. Rehg, and J. M. Rehg, arXiv, 2021.
CL scenarios
Survey presenting CL scenarios
CL benchmarks
Replay
Latent replay
Generative replay
L1, L2, Dropout
Regularization strategies
Architectural strategies
Hybrid strategies
Applications
, by M. Toneva, A. Sordoni, R. T. des Combes, A. Trischler, Y. Bengio, and G. J. Gordon, ICLR, 2019.
, by R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber, NIPS, 2013 (Permuted MNIST task).
, by G. M. van de Ven and A. S. Tolias, Continual Learning workshop at NeurIPS, 2018. task/domain/class incremental learning
, 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
, by R. Aljundi, K. Kelchtermans, and T. Tuytelaars, CVPR, 2019.
, by M. De Lange and T. Tuytelaars, ICCV, 2021. Data-incremental and comparisons with other CL scenarios
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.
, by V. Lomonaco and D. Maltoni, Proceedings of the 1st Annual Conference on Robot Learning, vol. 78, pp. 17–26, 2017.
, by Q. She et al. ICRA, 2020.
, by S. Stojanov et al., CVPR, 2019. CRIB benchmark
, by R. Roady, T. L. Hayes, H. Vaidya, and C. Kanan, CVPR 2019.
, by A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny, ICLR, 2019. Evaluation protocol with "split by experiences".
, by D. Lopez-Paz and M. Ranzato, NIPS, 2017. popular formalization of ACC, BWT, FWT.
, by M. Mundt, S. Lang, Q. Delfosse, and K. Kersting, arXiv, 2021.
, by N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, and D. Maltoni, arXiv, 2018. definition of additional metrics
, by A. Prabhu, P. H. S. Torr, and P. K. Dokania, ECCV, 2020.
, by R. Aljundi et al., NeurIPS, 2019.
, by Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni, IROS, 2020.
, by H. Shin, J. K. Lee, J. Kim, and J. Kim, NeurIPS, 2017.
, by G. M. van de Ven, H. T. Siegelmann, and A. S. Tolias, Nature Communications, 2020
, by Goodfellow et al, 2015.
, by Mirzadeh et al., NeurIPS, 2020.
, by Li et al., TPAMI 2017.
, by Kirkpatrick et al, PNAS 2017.
, by Zenke et al., 2017.
, by Von Osvald et al., ICLR 2020.
, by Lomonaco et al, CLVision Workshop at CVPR 2020. CWR*
, by Rusu et al., arXiv, 2016.
, by Mallya et al., CVPR, 2018.
, by Serra et al., ICML, 2018.
, by Wortsman et al., NeurIPS, 2020.
, by Lopez-Paz et al, NeurIPS 2017 GEM.
, by Rebuffi et al, CVPR, 2017.
, by Schwarz et al, ICML, 2018.
, by L. Pellegrini et al., IROS 2020 AR1*.
, by L. Pellegrini et al., ESANN, 2021.
by T. Diethe et al., Continual Learning Workshop at NeurIPS, 2018.
Startups / Companies: , ,
Tools / Libraries: , , ,
, by Hadsell et al., Trends in Cognitive Science, 2020. Continual meta learning - Meta continual learning
, by Khetarpal et al, arXiv, 2020.
, by D. Rao et al., NeurIPS 2019.
Distributed Continual Learning , by Carta et al., arXiv, 2021.
Continual Sequence Learning , by Cossu et al, Neural Networks, vol. 143, pp. 607–627, 2021. , by Cossu et al., ESANN, 2021.
, the software library based on PyTorch used for the coding session of this course.
, coding continual learning from scratch in notebooks