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Multi-Scale Dense Network for Resource Efficient Image Classification

Paper Details

  • Authors: Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger
  • Link: Arxiv
  • Tags: Image Classification, Low Resource Deep Learning
  • Year: 2017
  • Conference: ICML 2018
  • Implementation: Official in Torch(Lua), Reproduced in PyTorch

Summary

Problem

Most deep learning frameworks take an enormous amount of resources even at test time, in terms of memory and time. In this work the authors propose a new perspective to look at this problem as a) anytime classification b) budgeted batch classification. Further, they propose an image classifier called MSD-Net that gives good accuracies when placed in restrictions posed by these conditions.

How is it Solved

  • Some Definitions:

    • Anytime classification: Fixed time for prediction of each data point.
    • Budgeted batch classification: Fixed time for prediction of a batch of points, hence sharing of total time among all points.
    • Block: A group of layers that operate at separate scales (in this paper 3 different scales). Each block having a classifier at the end.

  • Some Related Recent Models:

    • Adaptive Neural Networks ( Bolukbasi et al. [1] ): Train multiple models of increasing capacity and sequentially evaluate them at test time. This is undesirable as there is no sharing between networks.
    • Classifiers after features of intermediate layers: Here classifiers are fitted after multiple layers of a network like ResNet or DenseNet. The authors show classifiers after layers producing fine features suffer in terms of accuracies.

  • Approach:

    • Problem 1 They note that coarse-level features are necessary for good classification, hence they create a multi-scale network with one block consisting of multiple layers that operate at different features.
    • Feature maps of a particular layer are made up of a) Regular convolution of the previous layer with same scale features b) Doing Strided Convolutions (i.e. stride = 2) over a layer with finer-scale feature maps.
    • This facilitates the transfer of information from one block to another. After the coarse-level features of each block is the classifier corresponding to that block.
    • Problem 2 The authors observed that adding classifiers in between layers of a network, compromised the accuracy of the layers after that. But this was not the case for DenseNet where each layer was connected with its subsequent layers.
    • Hence they used Dense Connections. This means that a layer can by-pass a previous layer that is optimized for short-term gain for improving the accuracy of an intermediate classifier.

  • Minor Points on Architecture:

    • Rhe First layer consists of lateral connections within the block.
    • Features at scale 's' are made up of concatenation of features of previous layers made at scale 's' and 's-1'
    • Each classifier has a parameter 'q' as output that represents its confidence. This can then be used for budgeted classification.

[1] Adaptive Neural Networks for Efficient Inference (link)