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Found 2 papers, 2 papers with code
Towards Understanding How Data Augmentation Works with Imbalanced Data
Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood.
Interpretable ML for Imbalanced Data
We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance.
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