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Author

Jie Yang

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Sep 2026

Geometry-aware neural density field for imbalanced classification via meta-learning-based oversampling

MGOML-NGF, a geometry-aware oversampling technique that integrates neural geometry fields with a meta-learning control mechanism to enable adaptive minority-sample generation, indicates that neural geometry-aware oversampling can improve minority-class representation while reducing artifacts caused by linear interpolat...

Touqeer Ahmad, Jie Yang · 0 citations
Jul 2026

GAN-powered oversampling with strengthened sample distribution for class overlapping imbalanced data

This study introduces a novel approach, named SDCGAN, which oversamples with a Conditional Generative Adversarial Network (CGAN) where the generator is fed with strengthened distribution information extracted from a curated set of minority samples.

Yu-Lin Zhang, Xiao-Zhe Wang, Kai-Wen Xue et al. · 0 citations

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