  Convolutional Neural Network (CNN) has been successful in image recognition
tasks, and recent works shed lights on how CNN separates different classes with
the learned inter-class knowledge through visualization. In this work, we
instead visualize the intra-class knowledge inside CNN to better understand how
an object class is represented in the fully-connected layers.
  To invert the intra-class knowledge into more interpretable images, we
propose a non-parametric patch prior upon previous CNN visualization models.
With it, we show how different "styles" of templates for an object class are
organized by CNN in terms of location and content, and represented in a
hierarchical and ensemble way. Moreover, such intra-class knowledge can be used
in many interesting applications, e.g. style-based image retrieval and
style-based object completion.
