CLASSIFICATION BASED ON MISSING FEATURES IN DEEP CONVOLUTIONAL NEURAL NETWORKS

Abstract

Artificial Neural Networks, notably Convolutional Neural Networks (CNN) are widely used for classification purposes in different fields such as image classification, text classification and others. It is not uncommon therefore that these models are used in critical systems (e.g. self-driving cars), where robustness is a very important attribute. All Convolutional Neural Networks used for classification, classify based on the extracted features found in the input sample. In this paper, we present a novel approach of doing the opposite - classification based on features not present in the input sample. Obtained results show not only that this way of learning is indeed possible but also that the trained models become more robust in certain scenarios. The presented approach can be applied to any existing Convolutional Neural Network model and does not require any additional training data.