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.

引入了一种名为 KeepOriginalAugment 的新型数据增强方法，可以使模型利用突出区域和非突出区域的多样性，在各项实验评估中显示出比现有技术更优越的性能。

KeepOriginalAugment: 基于单图像的改进信息保留数据增强方法

KeepOriginalAugment: Single Image-based Better Information-Preserving  Data Augmentation Approach

Data augmentation (DA) is an essential technique for training
state-of-the-art deep learning systems. In this paper, we empirically show data
augmentation might introduce noisy augmented examples and consequently hurt the
performance on unaugmented data during inference. To alleviate this issue, we
propose a simple yet highly effective approach, dubbed \emph{KeepAugment}, to
increase augmented images fidelity. The idea is first to use the saliency map
to detect important regions on the original images and then preserve these
informative regions during augmentation. This information-preserving strategy
allows us to generate more faithful training examples. Empirically, we
demonstrate our method significantly improves on a number of prior art data
augmentation schemes, e.g. AutoAugment, Cutout, random erasing, achieving
promising results on image classification, semi-supervised image
classification, multi-view multi-camera tracking and object detection.

本文提出了一种基于显著性图的数据增强方法 KeepAugment，通过保留图像中的重要区域进行增强，从而提高了深度学习系统的性能，在图像分类、半监督图像分类、多视角多摄像头跟踪和目标检测等领域获得了显著的结果。