# Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences

## TL;DR

This paper proposes that incorporating stochastic latent spaces and the information bottleneck principle into deep networks enhances their ability to generalize in inverse image sequence reconstruction tasks, demonstrated on cardiac potential data.

## Contribution

The paper introduces a novel variational approach combining stochastic latent spaces and the information bottleneck to improve generalization in inverse image reconstruction networks.

## Key findings

- Stochastic latent spaces improve test data generalization.
- Information bottleneck minimizes irrelevant input information.
- The proposed method outperforms deterministic models in cardiac potential reconstruction.

## Abstract

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.