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Keyword: rectified linear units ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

   He, Zhang, Ren and Sun · arXiv (Cornell University) · 2015

   The paper proposes PReLU, a generalised rectifier, and a initialisation method for rectifier networks, reaching 4.94% top-5 error on ImageNet 2012, which it says is the first result to surpass human-level performance.

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   1. UncheckedThe authors say PReLU, a adjustable rectifier unit, improves how well a network fits its data at almost no extra computing cost and with little risk of overfitting.“PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk.”
   2. UncheckedA new initialisation method designed for rectifier units lets very deep rectified networks be trained from scratch, and allows deeper or wider designs to be explored.“This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures.”
   3. UncheckedUsing PReLU networks, the authors report a 4.94% top-5 test error on the ImageNet 2012 image classification dataset.“Based on our PReLU networks (PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset.”