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The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization by@computational
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The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization

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Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
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Authors:

(1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands

(2) Andrey Rass, Den Haag, Netherlands.

2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture

This section details our study’s data-centric and model-centric analysis of the phenomena originally observed in (Balestriero, Bottou, and LeCun 2022). Firstly, we establish a practical framework for replicating such experiments in Section 2.1. Following this, we use a ResNet50 model trained from scratch with the Random Cropping and Random Horizontal Flip DA to provide the data-centric analysis of DA-induced class-specific bias on three datasets (Fashion-MNIST, CIFAR-10 and CIFAR-100) in Section 2.2. We then take a step back in Section 2.3 to evaluate the potential side effects of including the Random Horizontal Flip augmentation, as done in the original study. Finally, we conclude by demonstrating how alternate computer vision architectures interact with the phenomenon illustrated in previous sections. These findings are key as they serve to deepen our understanding of the potential pitfalls of introducing DA to computer vision tasks in order to improve overall model performance, while showing how the problem of class-specific bias can be alleviated or forestalled.


This paper is available on arxiv under CC BY 4.0 DEED license.