  The use of distributions and high-level features from deep architecture has
become commonplace in modern computer vision. Both of these methodologies have
separately achieved a great deal of success in many computer vision tasks.
However, there has been little work attempting to leverage the power of these
to methodologies jointly. To this end, this paper presents the Deep Mean Maps
(DMMs) framework, a novel family of methods to non-parametrically represent
distributions of features in convolutional neural network models.
  DMMs are able to both classify images using the distribution of top-level
features, and to tune the top-level features for performing this task. We show
how to implement DMMs using a special mean map layer composed of typical CNN
operations, making both forward and backward propagation simple.
  We illustrate the efficacy of DMMs at analyzing distributional patterns in
image data in a synthetic data experiment. We also show that we extending
existing deep architectures with DMMs improves the performance of existing CNNs
on several challenging real-world datasets.
