Hardness-Based Resampling reduces class recall gaps in balanced datasets by up to 32% on CIFAR-10 and 16% on CIFAR-100 by prioritizing harder samples over random or frequency-based selection.
Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization
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Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling
Hardness-Based Resampling reduces class recall gaps in balanced datasets by up to 32% on CIFAR-10 and 16% on CIFAR-100 by prioritizing harder samples over random or frequency-based selection.