  Supervised training of a convolutional network for object classification
should make explicit any information related to the class of objects and
disregard any auxiliary information associated with the capture of the image or
the variation within the object class. Does this happen in practice? Although
this seems to pertain to the very final layers in the network, if we look at
earlier layers we find that this is not the case. Surprisingly, strong spatial
information is implicit. This paper addresses this, in particular, exploiting
the image representation at the first fully connected layer, i.e. the global
image descriptor which has been recently shown to be most effective in a range
of visual recognition tasks. We empirically demonstrate evidences for the
finding in the contexts of four different tasks: 2d landmark detection, 2d
object keypoints prediction, estimation of the RGB values of input image, and
recovery of semantic label of each pixel. We base our investigation on a simple
framework with ridge rigression commonly across these tasks, and show results
which all support our insight. Such spatial information can be used for
computing correspondence of landmarks to a good accuracy, but should
potentially be useful for improving the training of the convolutional nets for
classification purposes.
