  Two major uncertainties, dataset bias and adversarial examples, prevail in
state-of-the-art AI algorithms with deep neural networks. In this paper, we
present an intuitive explanation for these issues as well as an interpretation
of the performance of deep networks in a natural-image space. The explanation
consists of two parts: the philosophy of neural networks and a hypothetical
model of natural-image spaces. Following the explanation, we 1) demonstrate
that the values of training samples differ, 2) provide incremental boost to the
accuracy of a CIFAR-10 classifier by introducing an additional "random-noise"
category during training, 3) alleviate over-fitting thereby enhancing the
robustness against adversarial examples by detecting and excluding illusive
training samples that are consistently misclassified. Our overall contribution
is therefore twofold. First, while most existing algorithms treat data equally
and have a strong appetite for more data, we demonstrate in contrast that an
individual datum can sometimes have disproportionate and counterproductive
influence and that it is not always better to train neural networks with more
data. Next, we consider more thoughtful strategies by taking into account the
geometric and topological properties of natural-image spaces to which deep
networks are applied.

Computer Vision and Pattern Recognition