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

Addressing Dataset Imbalance with Modified Generative Adversarial Networks

This paper introduces a modified Generative Adversarial Network (MGAN), which generates synthetic images of the minority class (128 × 128 × 3 pixels), thereby enhancing classifier resilience and surpasses conventional data balancing methods in medical imaging contexts.

SafeVAE-GAN: A Novel Re-sampling Approach for Multi-Class Imbalanced Data

Multi-class imbalanced datasets are ubiquitous in domains like medical diagnostics, fraud detection, and learning performance classification, where minority classes are critical but underrepresented, and class overlap introduces noise and ambiguous boundaries. Prior research has explored oversampling and undersamplin...

SMOG: an adaptive hybrid oversampling framework using SMOTE and conditional GAN for difficulty-aware learning on imbalanced data

SMOG, an adaptive hybrid oversampling framework that integrates the Synthetic Minority Over-sampling Technique with a Conditional Generative Adversarial Network (GAN)-based difficulty-aware learning strategy, highlights the effectiveness of adaptive hybrid generative strategies for intelligent learning on imbalanced da...

Adaptive Weighting–Synthetic Minority Oversampling Technique

Class imbalance is prevalent in real-world datasets. Minority samples are far fewer than majority samples. Traditional classifier design typically assumes balanced data, which causes classifiers to favor the majority class when faced with imbalanced datasets. Thus, there are high misclassification costs for minority cl...

SVED-SMOTE: support vector enhanced density SMOTE oversampling technique for imbalanced data classification

PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization

Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is to enrich the source distribution with augmented or generated samples, and recent text-to-image (T2I) diffusion models provide a strong generative prior for this purpos...