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    Deep learning· 2017

    MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

    Andrew G. Howard (first author) · 2017

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    arXiv 1704.04861OpenAlex W2612445135

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    Why it matters

    Established depthwise-separable convolution architectures as a standard for mobile neural inference.

    Condensed from the expert lists' notes.

    The record

    First released
    2017 (from arXiv 1704.04861)
    Citations
    26,359 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.454 · computed rank 288 · consensus 0.29, must-read 0, citations percentile 0.907 (2010s · deep-learning), awards 0, existing lists 0.5. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.