Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

42 papers of 8,653Sort Recent · Most cited
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    Few-Shot Class-Incremental Learning via Training-Free Prototype CalibrationQiwei Wang, Da-Wei Zhou, Yikai Zhang … Han-Jia YeNeurIPS
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    NPCL: Neural Processes for Uncertainty-Aware Continual LearningSaurav Jha, Dong Gong, He Zhao, Lina YaoNeurIPS
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    Fast Trainable Projection for Robust Fine-TuningJunjiao Tian, Yen‐Cheng Liu, Smith, James Seale, Zsolt KiraNeurIPS
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    Minimax Forward and Backward Learning of Evolving Tasks with Performance GuaranteesVerónica Álvarez, Santiago Mazuelas, José A. LozanoNeurIPS
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    Recasting Continual Learning as Sequence ModelingSoochan Lee, Jaehyeon Son, Gunhee KimNeurIPS
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    TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and PromotionPreetha Vijayan, Prashant Bhat, Elahe Arani, Bahram ZonoozNeurIPS
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    Prompt-augmented Temporal Point Process for Streaming Event SequenceSiqiao Xue, Yan Wang, Zhixuan Chu … Jun ZhouNeurIPS
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    FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual LearningDipam Goswami, Yuyang Liu, Bartłomiej Twardowski, Joost van de WeijerNeurIPS
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    A Definition of Continual Reinforcement LearningDavid Abel, André Barreto, Benjamin Van Roy … Satinder SinghNeurIPS
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    RanPAC: Random Projections and Pre-trained Models for Continual LearningMark D. McDonnell, Dong Gong, Amin Parveneh … Anton van den HengelNeurIPS
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    Learning to Modulate pre-trained Models in RLThomas Schmied, Markus Hofmarcher, Fabian Paischer … Sepp HochreiterNeurIPS
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    LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot LearningBo Liu, Yifeng Zhu, Chongkai Gao … Peter StoneNeurIPS
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    The Tunnel Effect: Building Data Representations in Deep Neural NetworksWojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani … T. P. TrzcinskiNeurIPS
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    Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World EnvironmentsThanh-Dat Truong, Hoang-Quan Nguyen, Bhiksha Raj, Khoa LuuNeurIPS
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    Deep Reinforcement Learning with Plasticity InjectionEvgenii Nikishin, Junhyuk Oh, Georg Ostrovski … André Sales BarretoNeurIPS
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    PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement LearningHojoon Lee, Jaegul Choo, Hyunseung Kim … Chulhee YunNeurIPS
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    An Efficient Dataset Condensation Plugin and Its Application to Continual LearningEnneng Yang, Li Shen, Zhenyi Wang … Guibing GuoNeurIPS
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    COOM: A Game Benchmark for Continual Reinforcement LearningTristan Tomilin, Meng Fang, Yudi Zhang, Mykola PechenizkiyNeurIPS
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    Nearly Optimal Bounds for Cyclic ForgettingWilliam Swartworth, Deanna Needell, Rachel Ward … Halyun JeongNeurIPS
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    Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation FrameworkWeiduo Liao, Ying Wei, Mingchen Jiang … Hisao IshibuchiNeurIPS
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    CLeAR: Continual Learning on Algorithmic Reasoning for Human-like IntelligenceBong Gyun Kang, Hyungi Kim, Dahuin Jung, Sungroh YoonNeurIPS
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.