  This study introduces a novel data augmentation technique, ADLDA, aimed at
mitigating the negative impact of data distribution shifts caused by the data
augmentation process in computer vision task. ADLDA partitions augmented data
into distinct subdomains and incorporates domain labels, combined with domain
adaptation techniques, to optimize data representation in the model's feature
space. Experimental results demonstrate that ADLDA significantly enhances model
performance across multiple datasets, particularly in neural network
architectures with complex feature extraction layers. Furthermore, ADLDA
improves the model's ability to locate and recognize key features, showcasing
potential in object recognition and image segmentation tasks. This paper's
contribution provides an effective data augmentation regularization method for
the field of computer vision aiding in the enhancement of robustness and
accuracy in deep learning models.
