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Multi-Scale Dense Convolutional Networks for Efficient Prediction

Abstract · Mar 29, 2017 00:19 ·

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Arxiv Abstract

  • Gao Huang
  • Danlu Chen
  • Tianhong Li
  • Felix Wu
  • Laurens van der Maaten
  • Kilian Q. Weinberger

This paper studies convolutional networks that require limited computational resources at test time. We develop a new network architecture that performs on par with state-of-the-art convolutional networks, whilst facilitating prediction in two settings: (1) an anytime-prediction setting in which the network’s prediction for one example is progressively updated, facilitating the output of a prediction at any time; and (2) a batch computational budget setting in which a fixed amount of computation is available to classify a set of examples that can be spent unevenly across ‘easier’ and ‘harder’ examples. Our network architecture uses multi-scale convolutions and progressively growing feature representations, which allows for the training of multiple classifiers at intermediate layers of the network. Experiments on three image-classification datasets demonstrate the efficacy of our architecture, in particular, when measured in terms of classification accuracy as a function of the amount of compute available.

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