Data augmentation (DA) enhances model generalization in computer vision but
may introduce biases, impacting class accuracy unevenly. Our study extends this
inquiry, examining DA's class-specific bias across various datasets, including
those distinct from ImageNet, through random cropping. We evaluated this
phenomenon with ResNet50, EfficientNetV2S, and SWIN ViT, discovering that while
residual models showed similar bias effects, Vision Transformers exhibited
greater robustness or altered dynamics. This suggests a nuanced approach to
model selection, emphasizing bias mitigation. We also refined a "data
augmentation robustness scouting" method to manage DA-induced biases more
efficiently, reducing computational demands significantly (training 112 models
instead of 1860; a reduction of factor 16.2) while still capturing essential
bias trends.

数据增强可以增强计算机视觉模型的泛化能力，但可能引入偏差，不均匀地影响类别准确性。我们的研究扩展了这一调查，通过随机裁剪考察了数据增强在不同数据集上的类别特定偏差，包括那些与 ImageNet 不同的数据集。我们使用 ResNet50、EfficientNetV2S 和 SWIN ViT 评估了这一现象，发现残差模型显示了类似的偏差效果，而视觉变换器表现出更高的稳健性或不同的动态变化。这表明在模型选择上需要一种细致入微的方法，强调偏差缓解。我们还改进了 “数据增强稳健性侦查” 方法，以更高效地管理数据增强引起的偏差，显著减少了计算需求（只需训练 112 个模型而不是 1860 个，减少了 16.2 倍），同时仍能捕捉到重要的偏差趋势。