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A list of papers, blogs, datasets and software in the field of lifelong/continual machine learning
| Date | Stars |
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| 2026-07-31 | 298 |
| 2026-08-05 | 298 |
| 2026-08-06 | 298 |
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# awesome-continual-learning / awesome-lifelong-learning The objective of continual learning is to have machines replicate human-like learning of being able to sequentially learn new tasks and observations while still being able to retain the knowledge obtained from past experiences. The following is a list of papers, blogs, datasets and software in the field of lifelong / continual / sequential / incremental machine learning. ## Contents - [Papers](#papers) - [Datasets](#dataset) - [Startups](#startups) - [Blogs](#blogs) - [Workshops](#workshops) # Papers ## Theory & Surveys - An empirical investigation of catastrophic forgetting in gradient-based neural networks. (2013) [[paper]](https://arxiv.org/abs/1312.6211) > *Talks about the problem of forgetting in neural nets and advantage of using dropout* - Catastrophic interference in connectionist networks: The sequential learning problem. (1989) [[paper]](https://www.sciencedirect.com/science/article/pii/S0079742108605368) > *One of the earliest papers introducing the concept of forgetting in learning modules* - Continual Lifelong Learning with Neural Networks: A Review (2018) [[paper]](https://arxiv.org/abs/1802.07569) > *An exhaustive survey paper on different approaches for continual or lifelong learning* - Making memories last: the synaptic tagging and capture hypothesis. (2011) [[paper]](https://www.ncbi.nlm.nih.gov/pubmed/21170072) > *A neuroscientific perspective on synaptic learning* - A massively parallel architecture for a self-organizing neural pattern recognition machine (1989) [[paper]](http://sites.bu.edu/steveg/files/2016/06/CarGro1987CVGIP.pdf) > *Talks about the tradeoff between stability (ability to preserve past knowledge) and plasticity (ability to rapidly learn new stuffs)* - Lifelong Machine Learning (2016) > *A book on this topic [[draft]](https://www.cs.uic.edu/~liub/lifelong-machine-learning-draft.pdf)* ## Approaches - Overcoming catastrophic forgetting in neural networks. (2016) [[paper]](https://arxiv.org/abs/1612.00796) [[Blog]](https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/) [[Unofficial Implementation]](https://github.com/ariseff/overcoming-catastrophic) > *Penalty applied in the learning process to restrict or consolidate those weights (EWC) that were important (by Fisher information matrix) for the older tasks to change* - Less-forgetting learning in deep neural networks (2016) [[paper]](https://arxiv.org/abs/1607.00122) > *Regularization based technique by discouraging the final hidden layer's neural representation to change much* - Learning without forgetting (2016) [[paper]](https://arxiv.org/pdf/1606.09282) [[Code]](https://github.com/lizhitwo/LearningWithoutForgetting) > *Uses knowledge distillation based regularization by trying to enforce that the predictions of the new data using the old task's neural parameters do not change much while sequentially learning from the new data only* - Gradient Episodic Memory for continual learning (2017) [[paper]](https://arxiv.org/abs/1706.08840) [[Code]](https://github.com/facebookresearch/GradientEpisodicMemory) > *explain. An efficient version has been recently proposed in this 2019 [[paper]](https://openreview.net/forum?id=Hkf2_sC5FX)* - iCaRL: Incremental Classifier and Representation Learning (2017) [[paper]](https://arxiv.org/abs/1611.07725) [[Code]](https://github.com/srebuffi/iCaRL) > *Uses herding to select a representative exemplar subset in the process of sequentially learning new classes of data* - Subset Replay based Continual Learning for Scalable Improvement of Autonomous Systems (2018) [[paper]](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w14/Brahma_Subset_Replay_Based_CVPR_2018_paper.pdf) > *Uses neural net's features to do a near online submodular subset selection of the previous examples and replays it during the newer learning sessions* - Continual learning with deep generative replay (2017) [[paper]](https://arxiv.org/ab
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