  Currently, the most successful learning models in computer vision are based
on learning successive representations followed by a decision layer. This is
usually actualized through feedforward multilayer neural networks, e.g.
ConvNets, where each layer forms one of such successive representations.
However, an alternative that can achieve the same goal is a feedback based
approach in which the representation is formed in an iterative manner based on
a feedback received from previous iteration's output.
  We establish that a feedback based approach has several fundamental
advantages over feedforward: it enables making early predictions at the query
time, its output naturally conforms to a hierarchical structure in the label
space (e.g. a taxonomy), and it provides a new basis for Curriculum Learning.
We observe that feedback networks develop a considerably different
representation compared to feedforward counterparts, in line with the
aforementioned advantages. We put forth a general feedback based learning
architecture with the endpoint results on par or better than existing
feedforward networks with the addition of the above advantages. We also
investigate several mechanisms in feedback architectures (e.g. skip connections
in time) and design choices (e.g. feedback length). We hope this study offers
new perspectives in quest for more natural and practical learning models.
