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Learning with Domain Knowledge to Develop Justifiable Convolutional Networks
Proceedings of The 14th Asian Conference on Machine
Learning, PMLR 189:64-79, 2023.
Abstract
The inherent structure of the Convolutional Neural
Networks (CNN) allows them to extract features that
are highly correlated with the classes while
performing image classification. However, it may
happen that the extracted features are merely
coincidental and may not be justifiable from a human
perspective. For example, from a set of images of
cows on grassland, CNN can erroneously extract grass
as the feature of the class cow. There are two main
limitations to this kind of learning: firstly, in
many false-negative cases, correct features will not
be used, and secondly, in false-positive cases the
system will lack accountability. There is no
implicit way to inform CNN to learn the features
that are justifiable from a human perspective to
resolve these issues. In this paper, we argue that
if we provide domain knowledge to guide the learning
process of CNN, it is possible to reliably learn the
justifiable features. We propose a systematic yet
simple mechanism to incorporate domain knowledge to
guide the learning process of the CNNs to extract
justifiable features. The flip side is that it needs
additional input. However, we have shown that even
with minimal additional input our method can
effectively propagate the knowledge within a class
during training. We demonstrate that justifiable
features not only enhance accuracy but also demand
less amount of data and training time. Moreover, we
also show that the proposed method is more robust
against perturbational changes in the input images.