Concepedia

Early Feature Aggregation

Early Feature Aggregation is a feature integration strategy in which input representations are combined at an early stage of the processing pipeline, prior to deep hierarchical feature learning, to enable richer initial feature interactions.

2

Publications

139

Citations

10

Authors

4

Institutions

Publications per year

2017–2026

1

Parents

Publications

2

1–3 of 2

Authors

10

  1. AD
    Alexey Dosovitskiy

    University of Freiburg, Germany

    1 publications · 109 citations

    1

    h-index

  2. SH
    Song Hu

    Shanghai Ocean University, China

    1 publications · 30 citations

    1

    h-index

  3. SK
    Simon Kornblith

    Google (United States), United States

    1 publications · 109 citations

    1

    h-index

  4. MR
    Maithra Raghu

    Google (United States), United States

    1 publications · 109 citations

    1

    h-index

  5. YH
    Yang Hong

    University of Oklahoma, United States

    1 publications · 30 citations

    1

    h-index

1–5 of 10

Institutions

4

  1. Google (United States)

    Mountain View, United States

    1 h-index

    4 publications

    436 citations

  2. Shanghai Ocean University

    Shanghai, China

    1 h-index

    5 publications

    150 citations

  3. University of Massachusetts Dartmouth

    North Dartmouth, United States

    1 h-index

    1 publications

    30 citations

  4. Cornell University

    Ithaca, United States

    1 h-index

    1 publications

    109 citations

  5. Tsinghua University

    Beijing, China

    1 publications

    14 citations

  6. The University of Sydney

    Sydney, Australia

    1 publications

    14 citations

  7. Tencent (China)

    Shenzhen, China

    1 publications

    14 citations

1–7 of 4

Venues

  1. Marine Pollution Bulletin

    1 publications · 30 citations

    1

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 109 citations

    1

    h-index

1–3