title: Transformational Sparse Coding
abstract: 
  A fundamental problem faced by object recognition systems is that
  objects and their features can appear in different locations, scales
  and orientations. Current deep learning methods attempt to achieve
  invariance to local translations via pooling, discarding the locations
  of features in the process.  Other approaches explicitly learn
  transformed versions of the same feature, leading to representations
  that quickly explode in size. Instead of discarding the rich and
  useful information about feature transformations to achieve
  invariance, we argue that models should learn object features
  conjointly with their transformations to achieve equivariance.  We
  propose a new model of unsupervised learning based on sparse coding
  that can learn object features jointly with their affine
  transformations directly from images. Results based on learning from
  natural images indicate that our approach
  matches the reconstruction quality of traditional sparse coding but
  with significantly fewer degrees of freedom while simultaneously
  learning transformations from data. These results open the door to
  scaling up unsupervised learning to allow deep feature+transformation
  learning in a manner consistent with the ventral+dorsal stream
  architecture of the primate visual cortex.
authors: Dimitrios C. Gklezakos Rajesh P. N. Rao
hIndex: Rajesh P. N. Rao 57