Course Materials

All the course material in one page!
Slides PDF
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.
Slides PDF
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).
Slides PDF
CL scenarios
​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
​Task-Free Continual Learning, by R. Aljundi, K. Kelchtermans, and T. Tuytelaars, CVPR, 2019.
​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.
CL benchmarks
​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.
​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.
Slides PDF
​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.
​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
Slides PDF
​GDumb: A Simple Approach that Questions Our Progress in Continual Learning, by A. Prabhu, P. H. S. Torr, and P. K. Dokania, ECCV, 2020.
​Online Continual Learning with Maximal Interfered Retrieval, by R. Aljundi et al., NeurIPS, 2019.
Latent replay
​Latent Replay for Real-Time Continual Learning, by Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni, IROS, 2020.
Generative replay
​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
Slides PDF
L1, L2, Dropout
Regularization strategies
​Learning without Forgetting, by Li et al., TPAMI 2017.
​Overcoming catastrophic forgetting in neural networks, by Kirkpatrick et al, PNAS 2017.
​Continual learning with hypernetworks, by Von Osvald et al., ICLR 2020.
Architectural strategies
​Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches, by Lomonaco et al, CLVision Workshop at CVPR 2020. CWR*
​Progressive Neural Networks, by Rusu et al., arXiv, 2016.
​Supermasks in Superposition, by Wortsman et al., NeurIPS, 2020.
Slides PDF
Hybrid strategies
​Gradient Episodic Memory for Continual Learning, by Lopez-Paz et al, NeurIPS 2017 GEM.
​Latent Replay for Real-Time Continual Learning, by L. Pellegrini et al., IROS 2020 AR1*.
​Continual Learning in Practice by T. Diethe et al., Continual Learning Workshop at NeurIPS, 2018.
Startups / Companies: CogitAI, Neurala, Gantry​
Tools / Libraries: Avalanche, Continuum, Sequoia, CL-Gym​
Slides PDF
​Embracing Change: Continual Learning in Deep Neural Networks, by Hadsell et al., Trends in Cognitive Science, 2020. Continual meta learning - Meta continual learning
​Continual Unsupervised Representation Learning, by D. Rao et al., NeurIPS 2019.
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.

​Invited Lectures​


​Avalanche: an End-to-End Library for Continual Learning, the software library based on PyTorch used for the coding session of this course.
​ContinualAI Colab notebooks, coding continual learning from scratch in notebooks