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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.

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2019 IEEE Access All papers Clear all
  1. 2019
    Incremental Learning in Deep Convolutional Neural Networks Using Partial Network SharingSyed Shakib Sarwar, Aayush Ankit, Kaushik RoyIEEE Access · Purdue University West Lafayette
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  2. 2019
    Challenges in Task Incremental Learning for Assistive RoboticsFan Feng, Rosa H. M. Chan, Xuesong Shi … Qi SheIEEE Access · City University of Hong Kong
  3. 2019
    Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class ClassifiersXin Ye, Qiuyu ZhuIEEE Access · Shanghai University
  4. 2019
    Learning Automata Based Incremental Learning Method for Deep Neural NetworksHaonan Guo, Shilin Wang, Jian‐Xun Fan, Shenghong LiIEEE Access · Shanghai Jiao Tong University · Beijing University of Posts and Telecommunications
  5. 2019
    Memorized Variational Continual Learning for Dirichlet Process MixturesYang Yang, Bo Chen, Hongwei LiuIEEE Access · Xidian University
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.