# Course Materials

### [Introduction & Motivation](/lectures/introduction.md)

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**Classic readings on catastrophic forgetting**

[Catastrophic Forgetting; Catastrophic Interference; Stability; Plasticity; Rehearsal.](http://www.tandfonline.com/doi/abs/10.1080/09540099550039318) by and Anthony Robins. *Connection Science*, 123--146, 1995.

[Using Semi-Distributed Representations to Overcome Catastrophic Forgetting in Connectionist Networks](https://www.aaai.org/Papers/Symposia/Spring/1993/SS-93-06/SS93-06-007.pdf) 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](https://www.cs.uic.edu/~liub/lifelong-machine-learning.html), Second Edition. by Zhiyuan Chen and Bing Liu. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2018.

### [*Understanding Catastrophic Forgetting*](/lectures/understanding-catastrophic-forgetting.md)

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Classic references on Catastrophic Forgetting provided above.

[Does Continual Learning = Catastrophic Forgetting?](http://arxiv.org/abs/2101.07295), by A. Thai, S. Stojanov, I. Rehg, and J. M. Rehg,  arXiv, 2021.

[An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://openreview.net/forum?id=BJlxm30cKm), by M. Toneva, A. Sordoni, R. T. des Combes, A. Trischler, Y. Bengio, and G. J. Gordon, ICLR, 2019.

[Compete to Compute](http://papers.nips.cc/paper/5059-compete-to-compute.pdf), by R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber, NIPS, 2013 (**Permuted MNIST task**).

### [*Scenarios & Benchmarks*](/lectures/scenarios-and-benchamarks.md)

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**CL scenarios**

[Three scenarios for continual learning](http://arxiv.org/abs/1904.07734)**,** 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](https://www.sciencedirect.com/science/article/pii/S0893608019300838), 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](https://openaccess.thecvf.com/content_CVPR_2019/papers/Aljundi_Task-Free_Continual_Learning_CVPR_2019_paper.pdf), by R. Aljundi, K. Kelchtermans, and T. Tuytelaars, CVPR, 2019.

[Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://arxiv.org/abs/2009.00919), 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](http://www.sciencedirect.com/science/article/pii/S1566253519307377) 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](http://proceedings.mlr.press/v78/lomonaco17a.html), 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](http://arxiv.org/abs/1911.06487), by Q. She et al. ICRA, 2020.

[Incremental Object Learning From Contiguous Views](https://openaccess.thecvf.com/content_CVPR_2019/html/Stojanov_Incremental_Object_Learning_From_Contiguous_Views_CVPR_2019_paper.htm), by S. Stojanov et al., CVPR, 2019. **CRIB benchmark**

[Stream-51: Streaming Classification and Novelty Detection From Videos](https://openaccess.thecvf.com/content_CVPRW_2020/html/w15/Roady_Stream-51_Streaming_Classification_and_Novelty_Detection_From_Videos_CVPRW_2020_paper.html), by R. Roady, T. L. Hayes, H. Vaidya, and C. Kanan, CVPR 2019.

### [*Evaluation & Metrics*](/lectures/evaluation-protocols-and-metrics.md)

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[Efficient Lifelong Learning with A-GEM](http://arxiv.org/abs/1812.00420), by A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny, ICLR, 2019.  **Evaluation protocol with "split by experiences"**.

[Gradient Episodic Memory for Continual Learning](https://arxiv.org/abs/1706.08840), 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](http://arxiv.org/abs/2110.03331), 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](http://arxiv.org/abs/1810.13166), by N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, and D. Maltoni, arXiv, 2018. **definition of additional metrics**

### [*Methodologies \[part 1\]*](/lectures/strategies.md)

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**Replay** 

[GDumb: A Simple Approach that Questions Our Progress in Continual Learning](http://openaccess.thecvf.com/content_cvpr_2017/papers/Rebuffi_iCaRL_Incremental_Classifier_CVPR_2017_paper.pdf), by A. Prabhu, P. H. S. Torr, and P. K. Dokania, ECCV, 2020.

[Online Continual Learning with Maximal Interfered Retrieval](https://proceedings.neurips.cc/paper/2019/hash/15825aee15eb335cc13f9b559f166ee8-Abstract.html), by R. Aljundi et al., NeurIPS, 2019. 

**Latent replay**

[Latent Replay for Real-Time Continual Learning](https://ras.papercept.net/images/temp/IROS/files/0596.pdf), by Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni,  IROS, 2020.

**Generative replay**

[Continual Learning with Deep Generative Replay](http://papers.nips.cc/paper/6892-continual-learning-with-deep-generative-replay.pdf), by H. Shin, J. K. Lee, J. Kim, and J. Kim, NeurIPS, 2017.

[Brain-inspired replay for continual learning with artificial neural networks](https://www.nature.com/articles/s41467-020-17866-2), by G. M. van de Ven, H. T. Siegelmann, and A. S. Tolias, Nature Communications, 2020 

### [*Methodologies \[part 2\]*](/lectures/methodologies-part-2.md)

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**L1, L2, Dropout**

[An Empirical Investigation of Cata](https://arxiv.org/abs/1312.6211)[trophic Forgetting in Gradient-Based Neural Networks](https://arxiv.org/abs/1312.6211), by Goodfellow et al, 2015.

[Understanding the Role of Training Regimes in Continual Learning](https://arxiv.org/pdf/2006.06958.pdf), by Mirzadeh et al., NeurIPS, 2020.

**Regularization strategies**

[Learning without Forgetting](https://arxiv.org/pdf/1606.09282.pdf), by Li et al., TPAMI 2017.

[Overcoming catastrophic forgetting in neural networks](https://arxiv.org/pdf/1612.00796.pdf), by Kirkpatrick et al, PNAS 2017.

[Continual Learning Through Synaptic Intelligence](https://arxiv.org/pdf/1703.04200.pdf), by Zenke et al., 2017.

[Continual learning with hypernetworks](https://arxiv.org/abs/1906.00695), by Von Osvald et al., ICLR 2020.

**Architectural strategies**

[Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches](https://arxiv.org/pdf/1907.03799.pdf), by Lomonaco et al, CLVision Workshop at CVPR 2020. **CWR\***

[Progressive Neural Networks](https://arxiv.org/abs/1606.04671), by Rusu et al., arXiv, 2016.

[PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://arxiv.org/abs/1711.05769), by Mallya et al., CVPR,  2018.

[Overcoming catastrophic forgetting with hard attention to the task](https://arxiv.org/abs/1801.01423), by Serra et al., ICML, 2018**.**

[Supermasks in Superposition](https://arxiv.org/pdf/2006.14769.pdf), by Wortsman et al., NeurIPS, 2020.

### [*Methodologies \[part 3\] & Applications*](/lectures/methodologies-part-3-applications-and-tools.md)

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**Hybrid strategies**

[Gradient Episodic Memory for Continual Learning](https://arxiv.org/abs/1706.08840), by Lopez-Paz et al, NeurIPS 2017 **GEM**.

[iCaRL: Incremental Classifier and Representation Learning](https://arxiv.org/abs/1611.07725), by Rebuffi et al, CVPR, 2017.

[Progress & Compress: A scalable framework for continual learning](#invited-lectures), by Schwarz et al, ICML, 2018.

[Latent Replay for Real-Time Continual Learning](http://ras.papercept.net/images/temp/IROS/files/0596.pdf), by L. Pellegrini et al., IROS 2020 **AR1\***.

**Applications**

[Continual Learning at the Edge: Real-Time Training on Smartphone Devices](https://arxiv.org/abs/2105.13127)**,** by L. Pellegrini et al.,  ESANN, 2021.

[Continual Learning in Practice](https://arxiv.org/abs/1903.05202v2) by T. Diethe et al., Continual Learning Workshop at NeurIPS, 2018.

**Startups / Companies:** [CogitAI](https://www.cogitai.com/), [Neurala](https://www.neurala.com/), [Gantry](https://gantry.io)

**Tools / Libraries:** [Avalanche](https://avalanche.continualai.org/), [Continuum](https://github.com/Continvvm/continuum), [Sequoia](https://github.com/lebrice/Sequoia), [CL-Gym](https://github.com/imirzadeh/CL-Gym)

### [*Frontiers in Continual Learning*](/lectures/frontiers-in-continual-learning.md)

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[Embracing Change: Continual Learning in Deep Neural Networks](https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613\(20\)30219-9), by Hadsell et al., Trends in Cognitive Science, 2020. **Continual meta learning - Meta continual learning**

[Towards Continual Reinforcement Learning: A Review and Perspectives](https://arxiv.org/abs/2012.13490), by Khetarpal et al, arXiv, 2020. 

[Continual Unsupervised Representation Learning](https://proceedings.neurips.cc/paper/2019/file/861578d797aeb0634f77aff3f488cca2-Paper.pdf)**,** by D. Rao et al., NeurIPS 2019.

**Distributed Continual Learning**\
[Ex-Model: Continual Learning from a Stream of Trained Models](http://arxiv.org/abs/2112.06511), by Carta et al., arXiv, 2021. 

**Continual Sequence Learning**\
[Continual learning for recurrent neural networks: An empirical evaluation](https://www.sciencedirect.com/science/article/abs/pii/S0893608021002847), by Cossu et al, Neural Networks, vol. 143, pp. 607–627, 2021.\
[Continual Learning with Echo State Networks](http://arxiv.org/abs/2105.07674), by Cossu et al., ESANN, 2021. 

### [*Invited Lectures*](/invited-lectures/invited-talks.md)

#### Software

[Avalanche: an End-to-End Library for Continual Learning](https://github.com/ContinualAI/avalanche), the software library based on PyTorch used for the coding session of this course.

[ContinualAI Colab notebooks](https://github.com/ContinualAI/colab), coding continual learning from scratch in notebooks


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