EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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- Published 2019-05-28 authorship evidence
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- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv · published 2019-05-28 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 7067ab18452b359bc1c38d448aba723f8f676267daa93f1f9b1d433f9223968c). Submission history as arXiv records it (UTC): v1 28 May 2019; v2 10 June 2019; v3 23 November 2019; v4 4 September 2020; v5 11 September 2020. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv · published 2019-05-28 · arXiv:1905.11946v1, submitted 28 May 2019 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 c74eb39a6142d448dc61c7ccb9afb1170f5a0509c0a02566f523ce56893bf80f). The author list is the byline as printed on the first page.
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- In its own wordsThe paper proposes a scaling method that uniformly scales network depth, width and resolution using what it calls a compound coefficient.“Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.” arXiv v1, PDF p. 1, abstract2019-05-28 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own wordsThe paper describes using neural architecture search to design a baseline network scaled up into a family of models called EfficientNets, which it reports achieve better accuracy and efficiency than previous ConvNets.“To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets.” arXiv v1, PDF p. 1, abstract2019-05-28 · publication · Stated in source · Supported Inspect claim & evidence ↗