no code implementations • 19 Mar 2024 • Tsz-Him Cheung, Dit-yan Yeung
Sample-mixing is a popular data augmentation approach that creates additional data by combining existing samples.
1 code implementation • ICLR 2022 • Tsz-Him Cheung, Dit-yan Yeung
However, the augmentation policies found are not adaptive to the dataset used, hindering the effectiveness of these AutoDA methods.
1 code implementation • ICLR 2021 • Tsz-Him Cheung, Dit-yan Yeung
Data augmentation is an efficient way to expand a training dataset by creating additional artificial data.