LEARNING MIXTURE MODELS OF SPATIAL COHERENCE
Becker, S. and Hinton, G.E.
We have previously described an unsupervised learning
procedure that discovers spatially coherent properties of the world by maximizing the
information that parameters extracted from different parts of the sensory input convey
about some common underlying cause. When given random dot stereograms of curved surfaces,
this procedure learns to extract surface depth because that is the property that is
coherent across space. It also learns how to interpolate the depth at one location from
the depths at nearby locations (Becker & Hinton, 1992). In this paper, we propose two
new models which handle surfaces with discontinuities. The first model attempts to detect
cases of discontinuities and reject them. The second model develops a mixture of expert
interpolators. It learns to detect the locations of discontinuities and to invoke
specialized, asymmetric interpolators that do not cross the discontinuities.
Download [ps] [pdf]
Neural Computation 5, 267-277 (1993)
[home page] [publications]