The content delves into the prevalence of fairness issues in image classification models, highlighting the impact of data diversity imbalance and model prediction bias. It explores the effects of data augmentation and representation learning on improving fairness and overall performance.
The study compares unfairness in balanced datasets with long-tailed recognition, emphasizing the importance of addressing problematic representation for fairer outcomes. Various techniques like contrastive learning and masked modeling are explored to enhance fairness in image classification models.
Key findings include the identification of extreme accuracy disparities among classes, the influence of class frequency on performance, and the role of data augmentation in promoting fairness. The content also discusses re-weighting methods and other strategies to address fairness issues in image recognition.
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by Jiequan Cui,... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18133.pdfDeeper Inquiries