Transformational Sparse Coding

Dimitrios C. Gklezakos, Rajesh P. N. Rao

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.

TL;DR: We extend sparse coding to include general affine transformations. We present a novel technical approach to circumvent inference intractability.

Conflicts: cs.washington.edu

Keywords: Unsupervised Learning, Computer vision, Optimization