  Autoencoders are data-specific compression algorithms learned automatically
from examples. The predominant approach has been to construct single large
global models that cover the domain. However, training and evaluating models of
increasing size comes at the price of additional time and computational cost.
Conditional computation, sparsity, and model pruning techniques can reduce
these costs while maintaining performance. Learning classifier systems (LCS)
are a framework for adaptively subdividing input spaces into an ensemble of
simpler local approximations that together cover the domain. LCS perform
conditional computation through the use of a population of individual
gating/guarding components, each associated with a local approximation. This
article explores the use of an LCS to adaptively decompose the input domain
into a collection of small autoencoders where local solutions of different
complexity may emerge. In addition to benefits in convergence time and
computational cost, it is shown possible to reduce code size as well as the
resulting decoder computational cost when compared with the global model
equivalent.
