  Neural networks represent data as projections on trained weights in a high
dimensional manifold. The trained weights act as a knowledge base consisting of
causal class dependencies. Inference built on features that identify these
dependencies is termed as feed-forward inference. Such inference mechanisms are
justified based on classical cause-to-effect inductive reasoning models.
Inductive reasoning based feed-forward inference is widely used due to its
mathematical simplicity and operational ease. Nevertheless, feed-forward models
do not generalize well to untrained situations. To alleviate this
generalization challenge, we propose using an effect-to-cause inference model
that reasons abductively. Here, the features represent the change from existing
weight dependencies given a certain effect. We term this change as contrast and
the ensuing reasoning mechanism as contrastive reasoning. In this paper, we
formalize the structure of contrastive reasoning and propose a methodology to
extract a neural network's notion of contrast. We demonstrate the value of
contrastive reasoning in two stages of a neural network's reasoning pipeline :
in inferring and visually explaining decisions for the application of object
recognition. We illustrate the value of contrastively recognizing images under
distortions by reporting an improvement of 3.47%, 2.56%, and 5.48% in average
accuracy under the proposed contrastive framework on CIFAR-10C, noisy STL-10,
and VisDA datasets respectively.
