Understand
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.