Incremental Learning

Incremental Learning is a machine learning paradigm in which a model progressively updates its knowledge by sequentially incorporating new data instances or tasks over time, without retraining from scratch, thereby maintaining computational efficiency and scalability in dynamic, large-scale data environments.

2.2K

Publications

134.2K

Citations

5.6K

Authors

1.5K

Institutions

Publications per year

2017–2026

1.1K

Authors

5.6K

  1. RP
    Robi Polikar

    Rowan University, United States

    20 publications · 3.1K citations

    18

    h-index

  2. CL
    C. L. Philip Chen

    South China University of Technology, China

    20 publications · 3.5K citations

    17

    h-index

  3. EL
    Edwin Lughofer

    Johannes Kepler University of Linz, Austria

    15 publications · 1.3K citations

    13

    h-index

  4. DZ
    Da-Wei Zhou

    Nanjing University, China

    14 publications · 1.3K citations

    13

    h-index

  5. SB
    Sujin Bureerat

    Khon Kaen University, Thailand

    13 publications · 432 citations

    12

    h-index

1–5 of 5.6K

Venues

  1. 30

    h-index

  2. arXiv (Cornell University)

    Repository · 83 publications · 2.6K citations

    27

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 25 publications · 3.6K citations

    23

    h-index

  4. IEEE Access

    34 publications · 1.2K citations

    23

    h-index

  5. Neurocomputing

    35 publications · 3.3K citations

    21

    h-index

1–5