  Feature disentanglement of the foreground target objects and the background
surrounding context has not been yet fully accomplished. The lack of network
interpretability prevents advancing for feature disentanglement and better
generalization robustness. We study the role of the context on interfering with
a disentangled foreground target object representation in this work. We
hypothesize that the representation of the surrounding context is heavily tied
with the foreground object due to the dense hierarchical parametrization of
convolutional networks with under-constrained learning algorithms. Working on a
framework that benefits from the bottom-up and top-down processing paradigms,
we investigate a systematic approach to shift learned representations in
feedforward networks from the emphasis on the irrelevant context to the
foreground objects. The top-down processing provides importance maps as the
means of the network internal self-interpretation that will guide the learning
algorithm to focus on the relevant foreground regions towards achieving a more
robust representations. We define an experimental evaluation setup with the
role of context emphasized using the MNIST dataset. The experimental results
reveal not only that the label prediction accuracy is improved but also a
higher degree of robustness to the background perturbation using various noise
generation methods is obtained.
