In the process of training convolutional neural networks, the training data is often insufficient to obtain ideal performance and encounters the overfitting problem. To address this issue, traditional data augmentation (DA) techniques, which are designed manually based on empirical results, are often adopted in supervised learning. Essentially, traditional DA techniques are in the implicit form of feature engineering. The augmentation strategies should be designed carefully, for example, the distribution of augmented samples should be close to the original data distribution. Otherwise, it will reduce the performance on the test set. Instead of designing augmentation strategies manually, we propose to learn the data distribution directly. New samples can then be generated from the estimated data distribution. Specifically, a deep DA framework is proposed which consists of two neural networks. One is a generative adversarial network, which is used to learn the data distribution, and the other one is a convolutional neural network classifier. We evaluate the proposed model on a handwritten Chinese character dataset and a digit dataset, and the experimental results show it outperforms baseline methods including one manually well-designed DA method and two state-of-the-art DA methods.
|Number of pages||17|
|Journal||Multimedia Tools and Applications|
|Early online date||13 Aug 2022|
|Publication status||Published - Dec 2022|
Bibliographical noteFunding Information:
The research of this work has been supported by the Dean’s Research Fund 2018-19 (FLASS/DRF/IDS-3), Departmental Collaborative Research Fund 2019 (MIT/DCRF-R2/18-19) of The Education University of Hong Kong, a grant from the Fundamental Research Funds for the Central Universities, China (Projects: 2022ECNU-HLYT001) and the Direct Grant (DR22A2) of Lingnan University, Hong Kong.
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Convolutional neural networks
- Data augmentation
- Generative adversarial networks