  Deep learning networks have shown state-of-the-art performance in many image
reconstruction problems. However, it is not well understood what properties of
representation and learning may improve the generalization ability of the
network. In this paper, we propose that the generalization ability of an
encoder-decoder network for inverse reconstruction can be improved in two
means. First, drawing from analytical learning theory, we theoretically show
that a stochastic latent space will improve the ability of a network to
generalize to test data outside the training distribution. Second, following
the information bottleneck principle, we show that a latent representation
minimally informative of the input data will help a network generalize to
unseen input variations that are irrelevant to the output reconstruction.
Therefore, we present a sequence image reconstruction network optimized by a
variational approximation of the information bottleneck principle with
stochastic latent space. In the application setting of reconstructing the
sequence of cardiac transmembrane potential from bodysurface potential, we
assess the two types of generalization abilities of the presented network
against its deterministic counterpart. The results demonstrate that the
generalization ability of an inverse reconstruction network can be improved by
stochasticity as well as the information bottleneck.
