A data-centric approach to class-specific bias in image data augmentation

• URL: http://arxiv.org/abs/2403.04120v1
• Date: Thu, 7 Mar 2024 00:32:47 GMT
• Title: A data-centric approach to class-specific bias in image data augmentation
• Authors: Athanasios Angelakis and Andrey Rass
• Abstract summary: Data augmentation (DA) enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly. We evaluate DA's class-specific bias across various datasets, including those distinct from ImageNet, through random cropping. This suggests a nuanced approach to model selection, emphasizing bias mitigation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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