What is the impact of flow-matching versus GAN-based augmentation on downstream classifier accuracy for imbala

Abstract

Abstract: Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision and have the potential to revolutionize robotics. However, the marked performance degradation to biases and imbalanced data questions the reliability of these methods. In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods. We introduce a joint dataset repairment strategy by combining a neural network

Research Question

What is the impact of flow-matching versus GAN-based augmentation on downstream classifier accuracy for imbalanced tabular datasets?

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