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continual-learning-papers

Continual Learning papers list, curated by ContinualAI
https://github.com/ContinualAI/continual-learning-papers

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List of papers
- Generative Replay Methods
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
  - Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
  - Generative Replay with Feedback Connections as a General Strategy for Continual Learning
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
  - Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory - -901, 2017.
- Bioinspired Methods
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - A Biologically Plausible Audio-Visual Integration Model for Continual Learning
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks
  - Storing Encoded Episodes as Concepts for Continual Learning
  - Cognitively-Inspired Model for Incremental Learning Using a Few Examples
  - Spiking Neural Predictive Coding for Continual Learning from Data Streams
  - Brain-like Replay for Continual Learning with Artificial Neural Networks
  - Selfless Sequential Learning
  - Backpropamine: Training Self-Modifying Neural Networks with Differentiable Neuromodulated Plasticity
  - Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations
  - Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting When Learning Cumulatively - -11, 2019. [fashion] [mnist] [sparsity]
  - FearNet: Brain-Inspired Model for Incremental Learning
  - SLAYER: Spike Layer Error Reassignment in Time - -1421, 2018.
  - Differentiable Plasticity: Training Plastic Neural Networks with Backpropagation - -3568, 2018.
  - Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization
  - Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World - -1702, 2017. [nlp] [vision]
  - Diffusion-Based Neuromodulation Can Eliminate Catastrophic Forgetting in Simple Neural Networks - -31, 2017.
  - How Do Neurons Operate on Sparse Distributed Representations? A Mathematical Theory of Sparsity, Neurons and Active Dendrites - -23, 2016. [hebbian] [sparsity]
  - Continuous Online Sequence Learning with an Unsupervised Neural Network Model - -2504, 2016. [spiking]
  - Backpropagation of Hebbian Plasticity for Continual Learning - Continual Learning*, 5, 2016.
  - Mitigation of Catastrophic Forgetting in Recurrent Neural Networks Using a Fixed Expansion Layer - -7, 2013. [mnist] [rnn] [sparsity]
  - Compete to Compute
  - Mitigation of Catastrophic Interference in Neural Networks Using a Fixed Expansion Layer - -729, 2012. [sparsity]
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Continuous Online Sequence Learning with an Unsupervised Neural Network Model - -2504, 2016. [spiking]
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
  - Synaptic Metaplasticity in Binarized Neural Networks
  - Synaptic Plasticity: Taming the Beast - -1183, 2000. [hebbian]
- Neuroscience
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Neural Inhibition for Continual Learning and Memory - -94, 2021.
  - Can Sleep Protect Memories from Catastrophic Forgetting?
  - Synaptic Consolidation: An Approach to Long-Term Learning - -257, 2012. [hebbian]
  - Negative Transfer Errors in Sequential Cognitive Skills: Strong-but-wrong Sequence Application. - -625, 2000.
  - Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions. - -308, 1990.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - The Organization of Behavior: A Neuropsychological Theory
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
  - Biological Underpinnings for Lifelong Learning Machines - Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. *Nature Machine Intelligence*, 196--210, 2022.
- Hybrid Methods
  - Toward Training Recurrent Neural Networks for Lifelong Learning - -35, 2019. [rnn]
  - Toward Training Recurrent Neural Networks for Lifelong Learning - -35, 2019. [rnn]
  - Dark Experience for General Continual Learning: A Strong, Simple Baseline - -15930, 2020.
  - Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches - -247, 2020. [core50]
  - Linear Mode Connectivity in Multitask and Continual Learning
  - Efficient Continual Learning in Neural Networks with Embedding Regularization - -148, 2020.
  - Efficient Lifelong Learning with A-GEM
  - Single-Net Continual Learning with Progressive Segmented Training (PST) - -1636, 2019. [cifar]
  - Continuous Learning in Single-Incremental-Task Scenarios - -73, 2019. [core50] [framework]
  - Lifelong Learning via Progressive Distillation and Retrospection
  - Gradient Episodic Memory for Continual Learning - Paz and Marc'Aurelio Ranzato. *NIPS*, 2017. [cifar] [mnist]
  - Progress & Compress: A Scalable Framework for Continual Learning - Barwinska, Yee Whye Teh, Razvan Pascanu and Raia Hadsell. *International Conference on Machine Learning*, 4528--4537, 2018. [vision]
  - Continual Learning of New Sound Classes Using Generative Replay
- Rehearsal Methods
  - Foundational Models for Continual Learning: An Empirical Study of Latent Replay
  - Using Hindsight to Anchor Past Knowledge in Continual Learning - Paz. *arXiv*, 2021.
  - Gradient Based Sample Selection for Online Continual Learning - -11825, 2019. [cifar] [mnist]
  - Selective Experience Replay for Lifelong Learning - Second AAAI Conference on Artificial Intelligence*, 3302--3309, 2018.
  - Brain-Inspired Replay for Continual Learning with Artificial Neural Networks
  - Continual Learning with Bayesian Neural Networks for Non-Stationary Data
  - Continual Learning with Hypernetworks
  - It's All About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning - Saez, Vladimir Araujo, Vincenzo Lomonaco and Davide Bacciu. , 2022.
  - Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams - -8259, 2021. [cifar] [framework] [mnist] [vision]
  - Replay in Deep Learning: Current Approaches and Missing Biological Elements - -2950, 2021.
  - Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification
  - Distilled Replay: Overcoming Forgetting through Synthetic Samples - Supervised Learning (CSSL) at IJCAI*, 2021.
  - Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning - -9394, 2021.
  - Online Coreset Selection for Rehearsal-based Continual Learning
  - CALM: Continuous Adaptive Learning for Language Modeling
  - ScaIL: Classifier Weights Scaling for Class Incremental Learning - 5, 2020*, 1255--1264, 2020.
  - REMIND Your Neural Network to Prevent Catastrophic Forgetting
  - GDumb: A Simple Approach That Questions Our Progress in Continual Learning - -540, 2020.
  - Graph-Based Continual Learning
  - Online Continual Learning with Maximal Interfered Retrieval - Caccia. *Advances in Neural Information Processing Systems 32*, 11849--11860, 2019. [cifar] [mnist]
  - Gradient Based Sample Selection for Online Continual Learning - -11825, 2019. [cifar] [mnist]
  - IL2M: Class Incremental Learning With Dual Memory - November 2, 2019*, 583--592, 2019.
  - On Tiny Episodic Memories in Continual Learning
  - Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
  - Memory Efficient Experience Replay for Streaming Learning
  - Prototype Reminding for Continual Learning - -10, 2019. [bayes] [cifar] [imagenet] [mnist]
  - Preventing Catastrophic Interference in MultipleSequence Learning Using Coupled Reverberating Elman Networks
- Meta Continual Learning
  - Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
  - Task Agnostic Continual Learning via Meta Learning
  - Learning to Continually Learn
  - Continual Learning with Deep Artificial Neurons
  - Meta-Consolidation for Continual Learning
  - Meta Continual Learning via Dynamic Programming
  - Online Meta-Learning
  - Meta-Learning Representations for Continual Learning
  - Meta Continual Learning - Yeon Cho, Daejoong Kim and Jiwon Kim. *arXiv*, 2018. [mnist]
- Architectural Methods
  - Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
  - Provable and Efficient Continual Representation Learning
  - The Multiple Subnetwork Hypothesis: Enabling Multidomain Learning by Isolating Task-Specific Subnetworks in Feedforward Neural Networks
  - Continual Learning with Node-Importance Based Adaptive Group Sparse Regularization
  - Structured Ensembles: An Approach to Reduce the Memory Footprint of Ensemble Methods - -418, 2021.
  - Continual Learning via Bit-Level Information Preserving - -16683, 2021.
  - SpaceNet: Make Free Space for Continual Learning - -11, 2021. [cifar] [fashion] [mnist] [sparsity]
  - Continual Learning in Recurrent Neural Networks
  - Modular Dynamic Neural Network: A Continual Learning Architecture
  - Continual Learning with Adaptive Weights (CLAW)
  - Continual Learning with Gated Incremental Memories for Sequential Data Processing
  - Explainability in Deep Reinforcement Learning - Rodr\ǵuez. *arXiv:2008.06693 [cs]*, 2020.
  - A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
  - Bayesian Nonparametric Weight Factorization for Continual Learning - -17, 2020. [bayes] [cifar] [mnist] [sparsity]
  - Efficient Continual Learning with Modular Networks and Task-Driven Priors
  - Progressive Memory Banks for Incremental Domain Adaptation
  - Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments - -674, 2019. [mnist]
  - Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
  - Towards AutoML in the Presence of Drift: First Results - Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon and Michele Sebag. *arXiv*, 2019.
  - Continual Unsupervised Representation Learning
  - A Progressive Model to Enable Continual Learning for Semantic Slot Filling - -1284, 2019. [nlp]
  - Adaptive Compression-based Lifelong Learning
  - Frosting Weights for Better Continual Training - -510, 2019. [cifar] [mnist]
  - HOUDINI: Lifelong Learning as Program Synthesis - -8698, 2018.
  - Reinforced Continual Learning - -908, 2018. [cifar] [mnist]
  - Lifelong Learning With Dynamically Expandable Networks
  - Expert Gate: Lifelong Learning with a Network of Experts
  - Neurogenesis Deep Learning
  - Net2Net: Accelerating Learning via Knowledge Transfer
  - Knowledge Transfer in Deep Block-Modular Neural Networks - -279, 2015. [vision]
  - ELLA: An Efficient Lifelong Learning Algorithm - -515, 2013.
  - A Self-Organising Network That Grows When Required - -1058, 2002. [som]
  - Architecture Matters in Continual Learning
- Metrics and Evaluations
  - Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning
  - Continual Learning in Deep Networks: An Analysis of the Last Layer
  - Avalanche: An End-to-End Library for Continual Learning
  - CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability
  - Optimal Continual Learning Has Perfect Memory and Is NP-HARD
  - Don't Forget, There Is More than Forgetting: New Metrics for Continual Learning - Rodr\ǵuez, Vincenzo Lomonaco, David Filliat and Davide Maltoni. *arXiv*, 2018. [cifar] [framework]
  - Regularization Shortcomings for Continual Learning
  - Strategies for Improving Single-Head Continual Learning Performance - -460, 2019. [cifar] [mnist]
  - Towards Robust Evaluations of Continual Learning
  - Three Scenarios for Continual Learning
- Regularization Methods
  - Overcoming Catastrophic Forgetting with Hard Attention to the Task
  - Overcoming Catastrophic Forgetting in Neural Networks - Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran and Raia Hadsell. *PNAS*, 3521--3526, 2017. [mnist]
  - Contrastive Continual Learning with Feature Propagation
  - Gradient Projection Memory for Continual Learning
  - Modeling the Background for Incremental Learning in Semantic Segmentation - -9242, 2020.
  - PLOP: Learning without Forgetting for Continual Semantic Segmentation
  - Insights from the Future for Continual Learning
  - PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
  - Uncertainty-Guided Continual Learning with Bayesian Neural Networks
  - Continual Learning of Object Instances
  - Uncertainty-Based Continual Learning with Adaptive Regularization - -4402, 2019. [bayes] [cifar] [mnist]
  - Efficient Continual Learning in Neural Networks with Embedding Regularization
  - Task-Free Continual Learning
  - Learning without Memorizing - Chuan Peng, Ziyan Wu and Rama Chellappa. *CVPR*, 2019. [cifar]
  - Incremental Learning Techniques for Semantic Segmentation - 2019 International Conference on Computer Vision Workshop, ICCVW 2019*, 3205--3212, 2019.
  - Functional Regularisation for Continual Learning Using Gaussian Processes
  - Memory Aware Synapses: Learning What (Not) to Forget
  - Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence - -547, 2018.
  - Rotate Your Networks: Better Weight Consolidation and Less Catastrophic Forgetting - -2268, 2018. [cifar] [mnist]
  - Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
  - Overcoming Catastrophic Forgetting with Hard Attention to the Task
  - Continual Learning in Generative Adversarial Nets - -9, 2017. [mnist]
  - Overcoming Catastrophic Forgetting by Incremental Moment Matching - Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha and Byoung-Tak Zhang. *Advances in Neural Information Processing Systems*, 4653--4663, 2017. [bayes] [cifar] [mnist]
  - Lifelong Generative Modeling - -14, 2017. [fashion] [generative] [mnist]
  - Incremental Learning of Object Detectors without Catastrophic Forgetting - -3429, 2017.
  - Continual Learning Through Synaptic Intelligence - -3995, 2017. [cifar] [mnist]
  - Learning without Forgetting - -629, 2016. [imagenet]
- Review Papers and Books
  - Measuring Catastrophic Forgetting in Neural Networks - Second AAAI Conference on Artificial Intelligence*, 2018. [mnist]
  - Lifelong Machine Learning: A Paradigm for Continuous Learning - -361, 2017.
  - Continual Learning for Recurrent Neural Networks: An Empirical Evaluation - -627, 2021. [rnn]
  - A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
  - How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition
  - A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks - -54, 2021.
  - A Continual Learning Survey: Defying Forgetting in Classification Tasks
  - Replay in Deep Learning: Current Approaches and Missing Biological Elements
  - Embracing Change: Continual Learning in Deep Neural Networks
  - Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges - Rodr\ǵuez. *Information Fusion*, 52--68, 2020. [framework]
  - A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
  - A Review of Off-Line Mode Dataset Shifts - -27, 2020.
  - Lifelong Machine Learning, Second Edition
  - Measuring Catastrophic Forgetting in Neural Networks - Second AAAI Conference on Artificial Intelligence*, 2018. [mnist]
  - Incremental On-Line Learning: A Review and Comparison of State of the Art Algorithms - -1274, 2018.
  - A Comprehensive, Application-Oriented Study of Catastrophic Forgetting in DNNs
  - Born to Learn: The Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks - -67, 2018.
  - Avoiding Catastrophic Forgetting - -408, 2017.
  - Learning in Nonstationary Environments: A Survey - -25, 2015.
  - Catastrophic Forgetting; Catastrophic Interference; Stability; Plasticity; Rehearsal. - -146, 1995. [dual]
  - Generative Models from the Perspective of Continual Learning - Dupré, Michael Garcia-Ortiz, Andrei Stoian and David Filliat. *Proceedings of the International Joint Conference on Neural Networks*, 2018. [cifar] [generative] [mnist]
- Robotics
  - Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot
  - Online Continual Learning for Embedded Devices
  - Controlling Soft Robotic Arms Using Continual Learning - -5476, 2022.
  - Online Object and Task Learning via Human Robot Interaction
  - Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
  - A Lifelong Learning Perspective for Mobile Robot Control - -214, 1995.
  - Explanation-Based Neural Network Learning for Robot Control
- Applications
  - Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
  - The Traffic Flow Prediction Method Using the Incremental Learning-Based CNN-LTSM Model: The Solution of Mobile Application
  - Findings of the First Shared Task on Lifelong Learning Machine Translation - jussà, Fethi Bougares and Olivier Galibert. *Proceedings of the Fifth Conference on Machine Translation*, 56--64, 2020. [framework] [nlp]
  - Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders
  - Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem - -14460, 2020. [framework] [mnist] [vision]
  - Incremental Learning for End-to-End Automatic Speech Recognition
  - Neural Topic Modeling with Continual Lifelong Learning
  - CLOPS: Continual Learning of Physiological Signals
  - Clinical Applications of Continual Learning Machine Learning - -e281, 2020.
  - Continual Learning for Domain Adaptation in Chest X-ray Classification - -11, 2020. [vision]
  - Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis
  - RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning - -16748, 2020. [nlp]
  - Importance Driven Continual Learning for Segmentation Across Domains - Marie Rickmann, Abhijit Guha Roy and Christian Wachinger. *arXiv*, 1--10, 2020. [vision]
  - LAMOL: LAnguage MOdeling for Lifelong Language Learning - Keng Sun, Cheng-Hao Ho and Hung-Yi Lee. *ICLR*, 2020. [nlp]
  - Non-Parametric Adaptation for Neural Machine Translation
  - Episodic Memory in Lifelong Language Learning
  - Continual Adaptation for Efficient Machine Communication
  - Continual Learning for Sentence Representations Using Conceptors
  - Lifelong and Interactive Learning of Factual Knowledge in Dialogues - -31, 2019. [nlp]
  - Making Good on LSTMs' Unfulfilled Promise
  - Lifelong Learning for Scene Recognition in Remote Sensing Images - -1476, 2019. [vision]
  - Towards Continual Learning in Medical Imaging - -4, 2018. [vision]
  - Toward Continual Learning for Conversational Agents
  - Toward an Architecture for Never-Ending Language Learning - Fourth AAAI Conference on Artificial Intelligence*, 1306--1313, 2010. [nlp]
  - Principles of Lifelong Learning for Predictive User Modeling - -46, 2009.
- Continual Reinforcement Learning
  - Continual Learning through Evolvable Neural Turing Machines
  - Lifetime Policy Reuse and the Importance of Task Capacity
  - Continuous Coordination As a Realistic Scenario for Lifelong Learning - -8024, 2021.
  - Reducing Catastrophic Forgetting When Evolving Neural Networks
  - A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning - -5700, 2019.
  - Policy Consolidation for Continual Reinforcement Learning
  - Continual Learning Exploiting Structure of Fractal Reservoir Computing - -47, 2019. [rnn]
  - Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
  - Leaky Tiling Activations: A Simple Approach to Learning Sparse Representations Online
  - Continual Reinforcement Learning with Complex Synapses
  - Unicorn: Continual Learning with a Universal, Off-policy Agent - -17, 2018.
  - Lifelong Inverse Reinforcement Learning - -4513, 2018.
  - Stable Predictive Representations with General Value Functions for Continual Learning
  - Progressive Neural Networks
  - Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets. - -235, 2016. [nlp]
  - CHILD: A First Step Towards Continual Learning - -104, 1997.
  - Unsupervised Lifelong Learning with Curricula - -3545, 2021.
- Benchmarks
  - vCLIMB: A Novel Video Class Incremental Learning Benchmark
  - Is Class-Incremental Enough for Continual Learning?
  - A Procedural World Generation Framework for Systematic Evaluation of Continual Learning - Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track*, 2021.
  - Evaluating Online Continual Learning with CALM - Teodor Sorodoc and Tomas Mikolov. *arXiv*, 2020. [nlp] [rnn]
  - Continual Reinforcement Learning in 3D Non-Stationary Environments - -249, 2020.
  - Stream-51: Streaming Classification and Novelty Detection From Videos - -229, 2020.
  - OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning - -8, 2019. [vision]
  - Incremental Object Learning From Contiguous Views - -8786, 2019.
  - New Metrics and Experimental Paradigms for Continual Learning - -21123, 2018.
  - CORe50: A New Dataset and Benchmark for Continuous Object Recognition - -26, 2017. [vision]
- Catastrophic Forgetting Studies
  - Continual Learning in the Teacher-Student Setup: Impact of Task Similarity - -6119, 2021.
  - Understanding Continual Learning Settings with Data Distribution Drift Analysis
  - Wide Neural Networks Forget Less Catastrophically
  - Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
  - Does Continual Learning = Catastrophic Forgetting?
  - Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget - -9293, 2020. [vision]
  - Understanding the Role of Training Regimes in Continual Learning
  - Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization - Jin Choi and Daeyoung Kim. *arXiv*, 2020. [vision]
  - Toward Understanding Catastrophic Forgetting in Continual Learning
  - A Study on Catastrophic Forgetting in Deep LSTM Networks - -728, 2019. [rnn]
  - An Empirical Study of Example Forgetting during Deep Neural Network Learning
  - Localizing Catastrophic Forgetting in Neural Networks
  - An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
  - The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects
  - Catastrophic Forgetting in Connectionist Networks - 2) by and Robert French. *Trends in Cognitive Sciences*, 128--135, 1999. [sparsity]
- Classics
  - How Does a Brain Build a Cognitive Code? - -51, 1980.
  - Pseudo-Recurrent Connectionist Networks: An Approach to the 'Sensitivity-Stability' Dilemma - -380, 1997. [dual]
  - Is Learning The N-Th Thing Any Easier Than Learning The First? - -646, 1996. [vision]
  - Learning in the Presence of Concept Drift and Hidden Contexts - -101, 1996.
  - Using Semi-Distributed Representations to Overcome Catastrophic Forgetting in Connectionist Networks - -178, 1991. [sparsity]
  - Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions - -308, 1990.
  - The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network - -88, 1988.
- Continual Few Shot Learning
  - Few-Shot Continual Learning: A Brain-Inspired Approach
  - Defining Benchmarks for Continual Few-Shot Learning
  - La-MAML: Look-ahead Meta Learning for Continual Learning
  - iTAML: An Incremental Task-Agnostic Meta-learning Approach - --13597, 2020. [cifar] [imagenet]
  - Wandering within a World: Online Contextualized Few-Shot Learning
  - Few-Shot Class-Incremental Learning
  - Few-Shot Class-Incremental Learning via Feature Space Composition
- Continual Meta Learning
  - Continuous Meta-Learning without Tasks
  - Reconciling Meta-Learning and Continual Learning with Online Mixtures of Tasks - -9133, 2019. [bayes] [vision]
- Continual Sequential Learning
  - Continual Sequence Generation with Adaptive Compositional Modules
  - Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning
  - Organizing Recurrent Network Dynamics by Task-Computation to Enable Continual Learning
  - Compositional Language Continual Learning
  - Online Continual Learning on Sequences
  - Unsupervised Progressive Learning and the STAM Architecture
  - Semi-Supervised Tuning from Temporal Coherence - -2514, 2016.
  - Self-Refreshing Memory in Artificial Neural Networks: Learning Temporal Sequences without Catastrophic Forgetting - -99, 2004. [rnn]
  - Using Pseudo-Recurrent Connectionist Networks to Solve the Problem of Sequential Learning
- Dissertation and Theses
  - Knowledge Uncertainty and Lifelong Learning in Neural Systems
  - An Introduction to Lifelong Supervised Learning
  - Large-Scale Deep Class-Incremental Learning. (Apprentissage Incrémental Profond à Large ̧́helle)
  - Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
  - Open Set Classification for Deep Learning in Large-Scale and Continual Learning Models
  - Continual Learning in Neural Networks
  - Continual Deep Learning via Progressive Learning
  - Continual Learning with Deep Architectures
  - Explanation-Based Neural Network Learning: A Lifelong Learning Approach
  - Continual Learning in Reinforcement Environments
- Others
  - Dataset Knowledge Transfer for Class-Incremental Learning without Memory - 8, 2022*, 3311--3320, 2022.
  - Continual Novelty Detection
  - Co\$2̂\$L: Contrastive Continual Learning
  - Sustainable Artificial Intelligence through Continual Learning
  - Continual Backprop: Stochastic Gradient Descent with Persistent Randomness
  - Continuum: Simple Management of Complex Continual Learning Scenarios
  - Posterior Meta-Replay for Continual Learning
  - Rethinking the Representational Continuity: Towards Unsupervised Continual Learning
  - Representation Memorization for Fast Learning New Knowledge without Forgetting
  - Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference - -3532, 2021.
  - Active Class Incremental Learning for Imbalanced Datasets - ECCV 2020 Workshops - Glasgow, UK, August 23-28, 2020, Proceedings, Part VI*, 146--162, 2020.
  - Initial Classifier Weights Replay for Memoryless Class Incremental Learning - 10, 2020*, 2020.
  - Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning
  - Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis - -15, 2020. [core50] [imagenet]
  - Energy-Based Models for Continual Learning
  - Continual Learning Using Task Conditional Neural Networks
  - Mnemonics Training: Multi-Class Incremental Learning without Forgetting - An Liu, Yuting Su, Bernt Schiele and Qianru Sun. *arXiv*, 2020. [cifar] [imagenet]
  - Continual Universal Object Detection
  - Gradient Projection Memory for Continual Learning
  - Structured Compression and Sharing of Representational Space for Continual Learning
  - Gated Linear Networks - Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt and Marcus Hutter. *arXiv*, 2020.
  - Lifelong Graph Learning
  - Continual Learning in Practice
  - Superposition of Many Models into One
  - Dynamically Constraining Connectionist Networks to Produce Distributed, Orthogonal Representations to Reduce Catastrophic Interference - -340, 2019.
  - Continual Learning via Neural Pruning
  - BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach
  - Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild - -321, 2019.
  - Continual Learning Using Bayesian Neural Networks
  - Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
  - Continual Rare-Class Recognition with Emerging Novel Subclasses
  - Random Path Selection for Incremental Learning - -12679, 2019. [cifar] [imagenet] [mnist]
  - Improving and Understanding Variational Continual Learning - -17, 2019. [bayes] [mnist]
  - Continual Learning via Online Leverage Score Sampling
  - Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers - -42031, 2019. [cifar] [mnist]
  - Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
  - A Unifying Bayesian View of Continual Learning
  - Overcoming Catastrophic Interference Using Conceptor-Aided Backpropagation
  - DeeSIL: Deep-Shallow Incremental Learning - ECCV 2018 Workshops - Munich, Germany, September 8-14, 2018, Proceedings, Part II*, 151--157, 2018.
  - Less-Forgetful Learning for Domain Expansion in Deep Neural Networks - Second AAAI Conference on Artificial Intelligence*, 2018.
  - Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights - -88, 2018. [imagenet]
  - Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks - -189, 2018. [sparsity] [vision]
  - Variational Continual Learning
  - Task Agnostic Continual Learning Using Online Variational Bayes
  - Encoder Based Lifelong Learning - -1337, 2017. [imagenet] [vision]
  - Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios
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Categories

List of papers 387 Join the ContinualAI Zotero group 2

Sub Categories

Bioinspired Methods 53 Others 46 Architectural Methods 33 Regularization Methods 27 Rehearsal Methods 27 Applications 25 Neuroscience 21 Review Papers and Books 21 Generative Replay Methods 18 Continual Reinforcement Learning 17 Catastrophic Forgetting Studies 15 Hybrid Methods 13 Metrics and Evaluations 10 Benchmarks 10 Dissertation and Theses 10 Continual Sequential Learning 9 Meta Continual Learning 9 Classics 7 Robotics 7 Continual Few Shot Learning 7 Continual Meta Learning 2