Classification Bias on a Data Diet

Published in Conference on Parsimony and Learning (Recent Spotlight Track), 2023

In this work, we analyse the classification bias in image classification tasks on data diet, a state-of-the-art data sparsification technique. We suggest that imbalanced data distributions when created by informed data pruning algorithms like data diet can result in image classification models with more consistent class-wise generalization performance.