Data augmentation is an effective way to overcome the overfitting problem of deep learning models. However, most existing studies on data augmentation work on framelike data (e.g., images), and few ...

In recent years, machine learning (ML) models have achieved robust performance and effective generalization in real-world applications. While ML models often perform well on training data, they can ...

We further investigate three targeted data augmentation techniques which selectively expand the original training samples, leading to comparable or superior performance compared to vanilla data ...

Data augmentation is used to diversify the training dataset by applying transoformations. Two ways will be shown: * w keras preprocessing layer * w tf.image methods """ importmatplotlib. pyplotasplt ...

Data augmentation also reduces the costs associated with collecting and labeling data, enables rare event prediction, and strengthens data privacy. At the same time, the limitations of data ...