Some Improvements on Deep Convolutional Neural Network Based Image Classification

Andrew G. Howard

Published 2013 in International Conference on Learning Representations

ABSTRACT

Abstract: We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    International Conference on Learning Representations

  • Publication date

    2013-12-18

  • Fields of study

    Computer Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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