Is It Time to Redefine the Classification Task for Deep Neural Networks?

Deep neural networks (DNNs) is demonstrated to be vulnerable to the adversarial example, which is generated by adding small adversarial perturbation into the original legitimate example to cause the wrong outputs of DNNs. Nowadays, most works focus on the robustness of the deep model, while few works pay attention to the robustness of the learning task itself defined on DNNs. So we redefine this issue as the robustness of deep neural learning system. A deep neural learning system consists of the deep model and the learning task defined on the deep model. Moreover, the deep model is usually a deep neural network, involving the model architecture, data, training loss and training algorithm. We speculate that the vulnerability of the deep learning system also roots in the learning task itself. This paper defines the interval-label classification task for the deep classification system, whose labels are predefined non-overlapping intervals, instead of a fixed value (hard label) or probability vector (soft label). The experimental results demonstrate that the interval-label classification task is more robust than the traditional classification task while retaining accuracy.