A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory

To improve the performance of deep neural networks, the Mixup method has been proposed to alleviate their memorization issues and sensitivity to adversarial samples. This provides networks with better generalization abilities. The learning principle of Mixup is essentially to train deep neural networks for regularization tasks with a convex combination of the original feature vectors and their labels. However, soft labels are generated directly using the mixing ratio without dealing with the uncertain information generated during the mixing process. Therefore, this paper proposes a new data augmentation method based on Mixup and Dempster-Shafer theory called DS-Mixup, which is a regularizer that can express and deal with the uncertainty caused by ambiguity. This method uses interval numbers to generate mass functions of mixed samples to model the distribution of set-valued random variables; then, ambiguous decision spaces are constructed, and soft labels with single-element subsets and multielement subsets are generated to further improve the delineation of decision boundaries during the training process. In addition, an evidence neural network with DS-Mixup is designed in this paper to accomplish recognition or classification tasks. Experimental results obtained on multimedia datasets, including attribute, image, text and signal data, show that the proposed method achieves more effective data augmentation effects and further improves the performance of deep neural networks.