Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …

Effective data generation for imbalanced learning using conditional generative adversarial
networks | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索 …

Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …

Application of conditional Generative Adversarial Networks as oversampling method.
Generates minority class samples by recovering the training data distribution. Outperforms …