  In this paper, we present a strategy for training convolutional neural
networks to effectively resolve interference arising from competing hypotheses
relating to inter-categorical information throughout the network. The premise
is based on the notion of feature binding, which is defined as the process by
which activations spread across space and layers in the network are
successfully integrated to arrive at a correct inference decision. In our work,
this is accomplished for the task of dense image labelling by blending images
based on (i) categorical clustering or (ii) the co-occurrence likelihood of
categories. We then train a feature binding network which simultaneously
segments and separates the blended images. Subsequent feature denoising to
suppress noisy activations reveals additional desirable properties and high
degrees of successful predictions. Through this process, we reveal a general
mechanism, distinct from any prior methods, for boosting the performance of the
base segmentation and saliency network while simultaneously increasing
robustness to adversarial attacks.
