This repository is maintained by Massimo Caccia and Timothée Lesort don't hesitate to send us an email to collaborate or fix some entries ({massimo.p.caccia , t.lesort} at gmail.com). The automation script of this repo is adapted from Automatic_Awesome_Bibliography.
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- Classics
- Empirical Study
- Surveys
- Influentials
- New Settings or Metrics
- Regularization Methods
- Distillation Methods
- Rehearsal Methods
- Generative Replay Methods
- Dynamic Architectures or Routing Methods
- Hybrid Methods
- Continual Few-Shot Learning
- Meta-Continual Learning
- Lifelong Reinforcement Learning
- Continual Generative Modeling
- Applications
- Thesis
- Libraries
- Workshops
- Catastrophic forgetting in connectionist networks , (1999) by French, Robert M. [bib]
- Lifelong robot learning , (1995) by Thrun, Sebastian and Mitchell, Tom M [bib]
Argues knowledge transfer is essential if robots are to learn control with moderate learning times - Catastrophic Forgetting, Rehearsal and Pseudorehearsal , (1995) by * Anthony Robins * [bib]
- Catastrophic interference in connectionist networks: The sequential learning problem , (1989) by McCloskey, Michael and Cohen, Neal J [bib]
Introduces CL and reveals the catastrophic forgetting problem
- A comprehensive study of class incremental learning algorithms for visual tasks , (2021) by Eden Belouadah, Adrian Popescu and Ioannis Kanellos [bib]
- Online Continual Learning in Image Classification: An Empirical Survey, (2021) by Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim and Scott Sanner [bib]
- CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions, (2020) by Lomonaco, Vincenzo, Pellegrini, Lorenzo, Rodriguez, Pau, Caccia, Massimo, She, Qi, Chen, Yu, Jodelet, Quentin, Wang, Ruiping, Mai, Zheda, Vazquez, David and others [bib]
surveys the results of the first CL competition at CVPR - Continual learning: A comparative study on how to defy forgetting in classification tasks , (2019) by Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh and Tinne Tuytelaars [bib]
Extensive empirical study of CL methods (in the multi-head setting) - Three scenarios for continual learning , (2019) by van de Ven, Gido M and Tolias, Andreas S [bib]
An extensive review of CL methods in three different scenarios (task-, domain-, and class-incremental learning) - Continuous learning in single-incremental-task scenarios, (2019) by Maltoni, Davide and Lomonaco, Vincenzo [bib]
- Towards Robust Evaluations of Continual Learning , (2018) by Farquhar, Sebastian and Gal, Yarin [bib]
Proposes desideratas and reexamines the evaluation protocol - Catastrophic forgetting: still a problem for DNNs, (2018) by Pf"ulb, B, Gepperth, A, Abdullah, S and Krawczyk, A [bib]
- {Measuring Catastrophic Forgetting in Neural Networks}, (2017) by {Kemker}, R., {McClure}, M., {Abitino}, A. and {Hayes}, T. and {Kanan}, C. [bib]
- {CORe50: a New Dataset and Benchmark for Continuous Object Recognition} , (2017) by Vincenzo Lomonaco and Davide Maltoni [bib]
- An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks , (2013) by Goodfellow, I.~J., Mirza, M., Xiao, D., Courville, A. and Bengio, Y. [bib]
Investigates CF in neural networks
- Towards Continual Reinforcement Learning: A Review and Perspectives, (2020) by Khimya Khetarpal, Matthew Riemer, Irina Rish and Doina Precup [bib]
A review on continual reinforcement learning - Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges , (2020) by Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat and Natalia Díaz-Rodríguez [bib]
- A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning , (2020) by Mundt, Martin, Hong, Yong Won, Pliushch, Iuliia and Ramesh, Visvanathan [bib]
propose a consolidated view to bridge continual learning, active learning and open set recognition in DNNs - Continual Lifelong Learning in Natural Language Processing: A Survey , (2020) by Magdalena Biesialska, Katarzyna Biesialska, Marta R. Costa-jussà [bib]
An extensive review of CL in Natural Language Processing (NLP) - Continual lifelong learning with neural networks: A review , (2019) by German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan and Stefan Wermter [bib]
An extensive review of CL - {Incremental learning algorithms and applications} , (2016) by Gepperth, Alexander and Hammer, Barbara [bib]
A survey on incremental learning and the various applications fields
- {REMIND Your Neural Network to Prevent Catastrophic Forgetting} , (2020) by {Hayes}, Tyler L., {Kafle}, Kushal, {Shrestha}, Robik and {Acharya}, Manoj and {Kanan}, Christopher [bib]
- Efficient Lifelong Learning with A-GEM , (2019) by Chaudhry, Arslan, Ranzato, Marc’Aurelio, Rohrbach, Marcus and Elhoseiny, Mohamed [bib]
More efficient GEM; Introduces online continual learning - Towards Robust Evaluations of Continual Learning , (2018) by Farquhar, Sebastian and Gal, Yarin [bib]
Proposes desideratas and reexamines the evaluation protocol - Continual Learning in Practice , (2018) by Diethe, Tom, Borchert, Tom, Thereska, Eno, Pigem, Borja de Balle and Lawrence, Neil [bib]
Proposes a reference architecture for a continual learning system - Overcoming catastrophic forgetting in neural networks , (2017) by Kirkpatrick, James, Pascanu, Razvan, Rabinowitz, Neil, Veness, Joel, Desjardins, Guillaume, Rusu, Andrei A, Milan, Kieran, Quan, John, Ramalho, Tiago, Grabska-Barwinska, Agnieszka and others [bib]
- Gradient Episodic Memory for Continual Learning , (2017) by Lopez-Paz, David and Ranzato, Marc-Aurelio [bib]
A model that alliviates CF via constrained optimization - Continual learning with deep generative replay , (2017) by Shin, Hanul, Lee, Jung Kwon, Kim, Jaehong and Kim, Jiwon [bib]
Introduces generative replay - An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks , (2013) by Goodfellow, I.~J., Mirza, M., Xiao, D., Courville, A. and Bengio, Y. [bib]
Investigates CF in neural networks
- IIRC: Incremental Implicitly-Refined Classification , (2021) by Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani and Sarath Chandar [bib]
A setup and benchmark to evaluate lifelong learning models in more real-life aligned scenarios. - Wandering Within a World: Online Contextualized Few-Shot Learning , (2020) by Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer and Richard S. Zemel [bib]
proposes a new continual few-shot setting where spacial and temporal context can be leveraged to and unseen classes need to be predicted - Defining Benchmarks for Continual Few-Shot Learning , (2020) by Antoniou, Antreas, Patacchiola, Massimiliano, Ochal, Mateusz and Storkey, Amos [bib]
(title is a good enough summary) - Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning , (2020) by Caccia, Massimo, Rodriguez, Pau, Ostapenko, Oleksiy, Normandin, Fabrice, Lin, Min, Caccia, Lucas, Laradji, Issam, Rish, Irina, Lacoste, Alexandre, Vazquez, David and Charlin, Laurent [bib]
Proposes a new approach to CL evaluation more aligned with real-life applications, bringing CL closer to Online Learning and Open-World learning - Compositional Language Continual Learning , (2020) by Yuanpeng Li, Liang Zhao, Kenneth Church and Mohamed Elhoseiny [bib]
method for compositional continual learning of sequence-to-sequence models - A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning , (2020) by Mundt, Martin, Hong, Yong Won, Pliushch, Iuliia and Ramesh, Visvanathan [bib]
propose a consolidated view to bridge continual learning, active learning and open set recognition in DNNs
- Continual Learning with Bayesian Neural Networks for Non-Stationary Data , (2020) by Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt and Stephan Günnemann [bib]
continual learning for non-stationary data using Bayesian neural networks and memory-based online variational Bayes - Improving and Understanding Variational Continual Learning , (2019) by Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui and Richard E. Turner [bib]
Improved results and interpretation of VCL. - Uncertainty-based Continual Learning with Adaptive Regularization , (2019) by Ahn, Hongjoon, Cha, Sungmin, Lee, Donggyu and Moon, Taesup [bib]
Introduces VCL with uncertainty measured for neurons instead of weights. - Functional Regularisation for Continual Learning with Gaussian Processes , (2019) by Titsias, Michalis K, Schwarz, Jonathan, Matthews, Alexander G de G, Pascanu, Razvan and Teh, Yee Whye [bib]
functional regularisation for Continual Learning: avoids forgetting a previous task by constructing and memorising an approximate posterior belief over the underlying task-specific function - Task Agnostic Continual Learning Using Online Variational Bayes , (2018) by Chen Zeno, Itay Golan, Elad Hoffer and Daniel Soudry [bib]
Introduces an optimizer for CL that relies on closed form updates of mu and sigma of BNN; introduce label trick for class learning (single-head) - Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation , (2018) by Xu He and Herbert Jaeger [bib]
Conceptor-Aided Backprop (CAB): gradients are shielded by conceptors against degradation of previously learned tasks - Overcoming Catastrophic Forgetting with Hard Attention to the Task , (2018) by Serra, Joan, Suris, Didac, Miron, Marius and Karatzoglou, Alexandros [bib]
Introducing a hard attention idea with binary masks - Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence , (2018) by Chaudhry, Arslan, Dokania, Puneet K, Ajanthan, Thalaiyasingam and Torr, Philip HS [bib]
Formalizes the shortcomings of multi-head evaluation, as well as the importance of replay in single-head setup. Presenting an improved version of EWC. - Variational Continual Learning , (2018) by Cuong V. Nguyen, Yingzhen Li, Thang D. Bui and Richard E. Turner [bib]
- Progress & compress: A scalable framework for continual learning , (2018) by Schwarz, Jonathan, Luketina, Jelena, Czarnecki, Wojciech M, Grabska-Barwinska, Agnieszka, Teh, Yee Whye, Pascanu, Razvan and Hadsell, Raia [bib]
A new P\&C architecture; online EWC for keeping the knowledge about the previous task, knowledge for keeping the knowledge about the current task (Multi-head setting, RL) - Online structured laplace approximations for overcoming catastrophic forgetting, (2018) by Ritter, Hippolyt, Botev, Aleksandar and Barber, David [bib]
- Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients , (2018) by Chen, Yu, Diethe, Tom and Lawrence, Neil [bib]
Improves on VCL - Overcoming catastrophic forgetting in neural networks , (2017) by Kirkpatrick, James, Pascanu, Razvan, Rabinowitz, Neil, Veness, Joel, Desjardins, Guillaume, Rusu, Andrei A, Milan, Kieran, Quan, John, Ramalho, Tiago, Grabska-Barwinska, Agnieszka and others [bib]
- Memory Aware Synapses: Learning what (not) to forget , (2017) by Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach and Tinne Tuytelaars [bib]
Importance of parameter measured based on their contribution to change in the learned prediction function - Continual Learning Through Synaptic Intelligence , (2017) by *Zenke, Friedeman, Poole, Ben and Ganguli, Surya * [bib]
Synaptic Intelligence (SI). Importance of parameter measured based on their contribution to change in the loss. - Overcoming catastrophic forgetting by incremental moment matching, (2017) by Lee, Sang-Woo, Kim, Jin-Hwa, Jun, Jaehyun, Ha, Jung-Woo and Zhang, Byoung-Tak [bib]
- Dark Experience for General Continual Learning: a Strong, Simple Baseline , (2020) by Buzzega, Pietro, Boschini, Matteo, Porrello, Angelo, Abati, Davide and Calderara, Simone [bib]
- Online Continual Learning under Extreme Memory Constraints , (2020) by Fini, Enrico, Lathuilière, Stèphane, Sangineto, Enver, Nabi, Moin and Ricci, Elisa [bib]
Introduces Memory-Constrained Online Continual Learning, a setting where no information can be transferred between tasks, and proposes a distillation-based solution (Batch-level Distillation) - PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning , (2020) by Douillard, Arthur, Cord, Matthieu, Ollion, Charles, Robert, Thomas and Valle, Eduardo [bib]
Novel knowledge distillation that trades efficiently rigidity and plasticity to learn large amount of small tasks - Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild , (2019) by Lee, Kibok, Lee, Kimin, Shin, Jinwoo and Lee, Honglak [bib]
Introducing global distillation loss and balanced finetuning; leveraging unlabeled data in the open world setting (Single-head setting) - Large scale incremental learning , (2019) by Wu, Yue, Chen, Yinpeng, Wang, Lijuan, Ye, Yuancheng, Liu, Zicheng, Guo, Yandong and Fu, Yun [bib]
Introducing bias parameters to the last fully connected layer to resolve the data imbalance issue (Single-head setting) - Continual Reinforcement Learning deployed in Real-life using PolicyDistillation and Sim2Real Transfer, (2019) by *Kalifou, René Traoré, Caselles-Dupré, Hugo, Lesort, Timothée, Sun, Te, Diaz-Rodriguez, Natalia and Filliat, David * [bib]
- Lifelong learning via progressive distillation and retrospection , (2018) by Hou, Saihui, Pan, Xinyu, Change Loy, Chen, Wang, Zilei and Lin, Dahua [bib]
Introducing an expert of the current task in the knowledge distillation method (Multi-head setting) - End-to-end incremental learning , (2018) by Castro, Francisco M, Marin-Jimenez, Manuel J, Guil, Nicolas, Schmid, Cordelia and Alahari, Karteek [bib]
Finetuning the last fully connected layer with a balanced dataset to resolve the data imbalance issue (Single-head setting) - Learning without forgetting , (2017) by Li, Zhizhong and Hoiem, Derek [bib]
Functional regularization through distillation (keeping the output of the updated network on the new data close to the output of the old network on the new data) - icarl: Incremental classifier and representation learning , (2017) by Rebuffi, Sylvestre-Alvise, Kolesnikov, Alexander, Sperl, Georg and Lampert, Christoph H [bib]
Binary cross-entropy loss for representation learning & exemplar memory (or coreset) for replay (Single-head setting)
- Graph-Based Continual Learning , (2021) by Binh Tang and David S. Matteson [bib]
Use graphs to link saved samples and improve the memory quality. - Online Class-Incremental Continual Learning with Adversarial Shapley Value , (2021) by Dongsub Shim*, Zheda Mai*, Jihwan Jeong*, Scott Sanner, Hyunwoo Kim, Jongseong Jang [bib]
Use Shapley Value adversarially to select which samples to relay - Dark Experience for General Continual Learning: a Strong, Simple Baseline , (2020) by Buzzega, Pietro, Boschini, Matteo, Porrello, Angelo, Abati, Davide and Calderara, Simone [bib]
- GDumb: A Simple Approach that Questions Our Progress in Continual Learning , (2020) by Prabhu, Ameya, Torr, Philip HS and Dokania, Puneet K [bib]
introduces a super simple methods that outperforms almost all methods in all of the CL benchmarks. We need new better benchamrks - Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes , (2020) by Timothée Lesort [bib]
- Imbalanced Continual Learning with Partitioning Reservoir Sampling , (2020) by Kim, Chris Dongjoo, Jeong, Jinseo and Kim, Gunhee [bib]
- PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning , (2020) by Douillard, Arthur, Cord, Matthieu, Ollion, Charles, Robert, Thomas and Valle, Eduardo [bib]
Novel knowledge distillation that trades efficiently rigidity and plasticity to learn large amount of small tasks - {REMIND Your Neural Network to Prevent Catastrophic Forgetting} , (2020) by {Hayes}, Tyler L., {Kafle}, Kushal, {Shrestha}, Robik and {Acharya}, Manoj and {Kanan}, Christopher [bib]
- Efficient Lifelong Learning with A-GEM , (2019) by Chaudhry, Arslan, Ranzato, Marc’Aurelio, Rohrbach, Marcus and Elhoseiny, Mohamed [bib]
More efficient GEM; Introduces online continual learning - Orthogonal Gradient Descent for Continual Learning , (2019) by Mehrdad Farajtabar, Navid Azizan, Alex Mott and Ang Li [bib]
projecting the gradients from new tasks onto a subspace in which the neural network output on previous task does not change and the projected gradient is still in a useful direction for learning the new task - Gradient based sample selection for online continual learning , (2019) by Aljundi, Rahaf, Lin, Min, Goujaud, Baptiste and Bengio, Yoshua [bib]
sample selection as a constraint reduction problem based on the constrained optimization view of continual learning - Online Continual Learning with Maximal Interfered Retrieval , (2019) by Aljundi, Rahaf and
, Lucas, Belilovsky, Eugene, Caccia, Massimo, Lin, Min, Charlin, Laurent and Tuytelaars, Tinne [bib]
Controlled sampling of memories for replay to automatically rehearse on tasks currently undergoing the most forgetting - Online Learned Continual Compression with Adaptative Quantization Module , (2019) by Caccia, Lucas, Belilovsky, Eugene, Caccia, Massimo and Pineau, Joelle [bib]
Uses stacks of VQ-VAE modules to progressively compress the data stream, enabling better rehearsal - Large scale incremental learning , (2019) by Wu, Yue, Chen, Yinpeng, Wang, Lijuan, Ye, Yuancheng, Liu, Zicheng, Guo, Yandong and Fu, Yun [bib]
Introducing bias parameters to the last fully connected layer to resolve the data imbalance issue (Single-head setting) - Learning a Unified Classifier Incrementally via Rebalancing, (2019) by Hou, Saihui, Pan, Xinyu, Loy, Chen Change, Wang, Zilei and Lin, Dahua [bib]
- Continual Reinforcement Learning deployed in Real-life using PolicyDistillation and Sim2Real Transfer, (2019) by *Kalifou, René Traoré, Caselles-Dupré, Hugo, Lesort, Timothée, Sun, Te, Diaz-Rodriguez, Natalia and Filliat, David * [bib]
- Experience replay for continual learning , (2019) by Rolnick, David, Ahuja, Arun, Schwarz, Jonathan, Lillicrap, Timothy and Wayne, Gregory [bib]
- Gradient Episodic Memory for Continual Learning , (2017) by Lopez-Paz, David and Ranzato, Marc-Aurelio [bib]
A model that alliviates CF via constrained optimization - icarl: Incremental classifier and representation learning , (2017) by Rebuffi, Sylvestre-Alvise, Kolesnikov, Alexander, Sperl, Georg and Lampert, Christoph H [bib]
Binary cross-entropy loss for representation learning & exemplar memory (or coreset) for replay (Single-head setting) - Catastrophic Forgetting, Rehearsal and Pseudorehearsal , (1995) by * Anthony Robins * [bib]
- Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes , (2020) by Timothée Lesort [bib]
- Brain-Like Replay For Continual Learning With Artificial Neural Networks , (2020) by van de Ven, Gido M, Siegelmann, Hava T and Tolias, Andreas S [bib]
- Learning to remember: A synaptic plasticity driven framework for continual learning , (2019) by Ostapenko, Oleksiy, Puscas, Mihai, Klein, Tassilo, Jahnichen, Patrick and Nabi, Moin [bib]
introdudes Dynamic generative memory (DGM) which relies on conditional generative adversarial networks with learnable connection plasticity realized with neural masking - Generative Models from the perspective of Continual Learning , (2019) by Lesort, Timoth{'e}e, Caselles-Dupr{'e}, Hugo, Garcia-Ortiz, Michael, Goudou, Jean-Fran{\c c}ois and Filliat, David [bib]
Extensive evaluation of CL methods for generative modeling - Closed-loop Memory GAN for Continual Learning , (2019) by Rios, Amanda and Itti, Laurent [bib]
- Marginal replay vs conditional replay for continual learning , (2019) by Lesort, Timoth{'e}e, Gepperth, Alexander, Stoian, Andrei and Filliat, David [bib]
Extensive evaluation of generative replay methods - Generative replay with feedback connections as a general strategy for continual learning , (2018) by Michiel van der Ven and Andreas S. Tolias [bib]
smarter Generative Replay - Continual learning with deep generative replay , (2017) by Shin, Hanul, Lee, Jung Kwon, Kim, Jaehong and Kim, Jiwon [bib]
Introduces generative replay
- ORACLE: Order Robust Adaptive Continual Learning , (2019) by Jaehong Yoon and Saehoon Kim and Eunho Yang and Sung Ju Hwang [bib]
- Random Path Selection for Incremental Learning , (2019) by Jathushan Rajasegaran and
Munawar Hayat and
Salman H. Khan and
Fahad Shahbaz Khan and
Ling Shao [bib]
Proposes a random path selection algorithm, called RPSnet, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing and reuse - Learn to Grow: {A} Continual Structure Learning Framework for Overcoming Catastrophic Forgetting , (2019) by Xilai Li and Yingbo Zhou and Tianfu Wu and Richard Socher and Caiming Xiong [bib]
- Incremental Learning through Deep Adaptation , (2018) by Amir Rosenfeld and John K. Tsotsos [bib]
- Packnet: Adding multiple tasks to a single network by iterative pruning, (2018) by Mallya, Arun and Lazebnik, Svetlana [bib]
- Piggyback: Adapting a single network to multiple tasks by learning to mask weights, (2018) by Mallya, Arun, Davis, Dillon and Lazebnik, Svetlana [bib]
- Continual Learning in Practice , (2018) by Diethe, Tom, Borchert, Tom, Thereska, Eno, Pigem, Borja de Balle and Lawrence, Neil [bib]
Proposes a reference architecture for a continual learning system - Growing a brain: Fine-tuning by increasing model capacity, (2017) by Wang, Yu-Xiong, Ramanan, Deva and Hebert, Martial [bib]
- Lifelong learning with dynamically expandable networks, (2017) by Yoon, Jaehong, Yang, Eunho, Lee, Jeongtae and Hwang, Sung Ju [bib]
- Progressive Neural Networks , (2016) by {Rusu}, A.~A., {Rabinowitz}, N.~C., {Desjardins}, G. and
{Soyer}, H., {Kirkpatrick}, J., {Kavukcuoglu}, K. and
{Pascanu}, R. and {Hadsell}, R. [bib]
Each task have a specific model connected to the previous ones
- Continual learning with hypernetworks , (2020) by Johannes von Oswald, Christian Henning, João Sacramento and Benjamin F. Grewe [bib]
Learning task-conditioned hypernetworks for continual learning as well as task embeddings; hypernetwors offers good model compression. - Compacting, Picking and Growing for Unforgetting Continual Learning , (2019) by Hung, Ching-Yi, Tu, Cheng-Hao, Wu, Cheng-En, Chen, Chien-Hung, Chan, Yi-Ming and Chen, Chu-Song [bib]
Approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. All enforced in an iterative manner
- Wandering Within a World: Online Contextualized Few-Shot Learning , (2020) by Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer and Richard S. Zemel [bib]
proposes a new continual few-shot setting where spacial and temporal context can be leveraged to and unseen classes need to be predicted - Defining Benchmarks for Continual Few-Shot Learning , (2020) by Antoniou, Antreas, Patacchiola, Massimiliano, Ochal, Mateusz and Storkey, Amos [bib]
(title is a good enough summary) - Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning , (2020) by Caccia, Massimo, Rodriguez, Pau, Ostapenko, Oleksiy, Normandin, Fabrice, Lin, Min, Caccia, Lucas, Laradji, Issam, Rish, Irina, Lacoste, Alexandre, Vazquez, David and Charlin, Laurent [bib]
Proposes a new approach to CL evaluation more aligned with real-life applications, bringing CL closer to Online Learning and Open-World learning - Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling , (2019) by Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh and Yang Yang [bib]
- Online Meta-Learning , (2019) by Finn, Chelsea, Rajeswaran, Aravind, Kakade, Sham and Levine, Sergey [bib]
defines Online Meta-learning; propsoses Follow the Meta Leader (FTML) (~ Online MAML) - Reconciling meta-learning and continual learning with online mixtures of tasks , (2019) by Jerfel, Ghassen, Grant, Erin, Griffiths, Tom and Heller, Katherine A [bib]
Meta-learns a tasks structure; continual adaptation via non-parametric prior - Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL , (2019) by Anusha Nagabandi, Chelsea Finn and Sergey Levine [bib]
Formulates an online learning procedure that uses SGD to update model parameters, and an EM with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distribution - Task Agnostic Continual Learning via Meta Learning , (2019) by Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh and Razvan Pascanu [bib]
Introduces What & How framework; enables Task Agnostic CL with meta learned task inference
- La-MAML: Look-ahead Meta Learning for Continual Learning , (2020) by Gunshi Gupta, Karmesh Yadav and Liam Paull [bib]
Proposes an online replay-based meta-continual learning algorithm with learning-rate modulation to mitigate catastrophic forgetting - Learning to Continually Learn , (2020) by Beaulieu, Shawn, Frati, Lapo, Miconi, Thomas, Lehman, Joel, Stanley, Kenneth O, Clune, Jeff and Cheney, Nick [bib]
Follow-up of OML. Meta-learns an activation-gating function instead. - Meta-Learning Representations for Continual Learning , (2019) by Javed, Khurram and White, Martha [bib]
Introduces Learns how to continually learn (OML) i.e. learns how to do online updates without forgetting. - Meta-learnt priors slow down catastrophic forgetting in neural networks , (2019) by Spigler, Giacomo [bib]
Learning MAML in a Meta continual learning way slows down forgetting - Learning to learn without forgetting by maximizing transfer and minimizing interference , (2018) by Riemer, Matthew, Cases, Ignacio, Ajemian, Robert, Liu, Miao, Rish, Irina, Tu, Yuhai and Tesauro, Gerald [bib]
- Reset-Free Lifelong Learning with Skill-Space Planning , (2021) by Kevin Lu, Aditya Grover, Pieter Abbeel and Igor Mordatch [bib]
- Towards Continual Reinforcement Learning: A Review and Perspectives, (2020) by Khimya Khetarpal, Matthew Riemer, Irina Rish and Doina Precup [bib]
A review on continual reinforcement learning - Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges , (2020) by Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat and Natalia Díaz-Rodríguez [bib]
- Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL , (2019) by Anusha Nagabandi, Chelsea Finn and Sergey Levine [bib]
Formulates an online learning procedure that uses SGD to update model parameters, and an EM with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distribution - Continual Reinforcement Learning deployed in Real-life using PolicyDistillation and Sim2Real Transfer, (2019) by *Kalifou, René Traoré, Caselles-Dupré, Hugo, Lesort, Timothée, Sun, Te, Diaz-Rodriguez, Natalia and Filliat, David * [bib]
- Experience replay for continual learning , (2019) by Rolnick, David, Ahuja, Arun, Schwarz, Jonathan, Lillicrap, Timothy and Wayne, Gregory [bib]
- Continual Unsupervised Representation Learning , (2019) by Dushyant Rao, Francesco Visin, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu and Raia Hadsell [bib]
Introduces unsupervised continual learning (no task label and no task boundaries) - Generative Models from the perspective of Continual Learning , (2019) by Lesort, Timoth{'e}e, Caselles-Dupr{'e}, Hugo, Garcia-Ortiz, Michael, Goudou, Jean-Fran{\c c}ois and Filliat, David [bib]
Extensive evaluation of CL methods for generative modeling - Closed-loop Memory GAN for Continual Learning , (2019) by Rios, Amanda and Itti, Laurent [bib]
- Lifelong Generative Modeling , (2017) by Ramapuram, Jason, Gregorova, Magda and Kalousis, Alexandros [bib]
- CLOPS: Continual Learning of Physiological Signals , (2020) by Kiyasseh, Dani, Zhu, Tingting and Clifton, David A [bib]
a healthcare-specific replay-based method to mitigate destructive interference during continual learning - LAMAL: LAnguage Modeling Is All You Need for Lifelong Language Learning , (2020) by Fan-Keng Sun, Cheng-Hao Ho and Hung-Yi Lee [bib]
- Compositional Language Continual Learning , (2020) by Yuanpeng Li, Liang Zhao, Kenneth Church and Mohamed Elhoseiny [bib]
method for compositional continual learning of sequence-to-sequence models - Unsupervised real-time anomaly detection for streaming data , (2017) by Ahmad, Subutai, Lavin, Alexander, Purdy, Scott and Agha, Zuha [bib]
HTM applied to real-world anomaly detection problem - Continuous online sequence learning with an unsupervised neural network model , (2016) by Cui, Yuwei, Ahmad, Subutai and Hawkins, Jeff [bib]
HTM applied to a prediction problem of taxi passenger demand
- Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes , (2020) by Timothée Lesort [bib]
- Continual Learning with Deep Architectures , (2019) by Vincenzo Lomonaco [bib]
- Continual Learning in Neural Networks , (2019) by Aljundi, Rahaf [bib]
- Continual learning in reinforcement environments , (1994) by Ring, Mark Bishop [bib]
- Sequoia - Towards a Systematic Organization of Continual Learning Research , (2021) by Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Matthew Riemer, Pau Rodriguez, Julio Hurtado, Khimya Khetarpal, Timothée Lesort, Laurent Charlin, Irina Rish and Massimo Caccia [bib]
A library that unifies Continual Supervised and Continual Reinforcement Learning research - Avalanche: an End-to-End Library for Continual Learning , (2021) by Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Gabriele Graffieti and Antonio Carta [bib]
A library for Continual Supervised Learning - Continuous Coordination As a Realistic Scenario for Lifelong Learning , (2021) by Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville and Sarath Chandar [bib]
a multi-agent lifelong learning testbed that supports both zero-shot and few-shot settings. - Continuum, Data Loaders for Continual Learning , (2020) by Douillard, Arthur and Lesort, Timothée [bib]
A library proposing continual supervised learning scenarios and metrics. - Framework for Analysis of Class-Incremental Learning , (2020) by Masana, Marc, Liu, Xialei, Twardowski, Bartlomiej, Menta, Mikel, Bagdanov, Andrew D and van de Weijer, Joost [bib]
A library for Continual Class-Incremental Learning
- Workshop on Continual Learning at ICML 2020 , (2020) by Rahaf Aljundi, Haytham Fayek, Eugene Belilovsky, David Lopez-Paz, Arslan Chaudhry, Marc Pickett, Puneet Dokania, Jonathan Schwarz, Sayna Ebrahimi [bib]
- 4th Lifelong Machine Learning Workshop at ICML 2020 , (2020) by Shagun Sodhani, Sarath Chandar, Balaraman Ravindran and Doina Precup [bib]
- 1st Lifelong Learning for Machine Translation Shared Task at WMT20 (EMNLP 2020) , (2020) by Loïc Barrault, Magdalena Biesialska, Marta R. Costa-jussà, Fethi Bougares, Olivier Galibert [bib]