An Autuencoder-based Data Augmentation Strategy for Generalization Improvement of DCNNs

摘要

Inspired by the phenomenon that the decoding weights of a well-trained autoencoder contain the information of the training samples, we proposed a data augmentation method by utilizing the decoding weights. Given a batch of training data, the autoencoder is trained and the decoding weights are activated; the decoding weights are then combined with the raw samples to generate augmented samples. Furthermore, we probe its working mechanism in three ways: (i) we prove that the decoding weights and the raw samples are of linear relationship under the transformation of a certain invertible function; (ii) the proposed method can sample from a larger range in both feature dimensions and label dimension, which can be interpreted as a broader distribution vicinity compared with those by other approaches; (iii) the model trained with our data augmentation approach has better representation capability, which is reflected by the higher Fisher's criteria value in deep feature space. We conduct extensive experiments on image and tabular dataset with multiple network architectures. The proposed method provides significant generalization performance improvement compared with the baseline and better or comparable performance compared with the other state-of-the-art data augmentation approaches. (C) 2020 Elsevier B.V. All rights reserved.