  Advanced image data augmentation techniques play a pivotal role in enhancing
the training of models for diverse computer vision tasks. Notably, SalfMix and
KeepAugment have emerged as popular strategies, showcasing their efficacy in
boosting model performance. However, SalfMix reliance on duplicating salient
features poses a risk of overfitting, potentially compromising the model's
generalization capabilities. Conversely, KeepAugment, which selectively
preserves salient regions and augments non-salient ones, introduces a domain
shift that hinders the exchange of crucial contextual information, impeding
overall model understanding. In response to these challenges, we introduce
KeepOriginalAugment, a novel data augmentation approach. This method
intelligently incorporates the most salient region within the non-salient area,
allowing augmentation to be applied to either region. Striking a balance
between data diversity and information preservation, KeepOriginalAugment
enables models to leverage both diverse salient and non-salient regions,
leading to enhanced performance. We explore three strategies for determining
the placement of the salient region minimum, maximum, or random and investigate
swapping perspective strategies to decide which part (salient or non-salient)
undergoes augmentation. Our experimental evaluations, conducted on
classification datasets such as CIFAR-10, CIFAR-100, and TinyImageNet,
demonstrate the superior performance of KeepOriginalAugment compared to
existing state-of-the-art techniques.
