1.2k citations · 1.5k across the 11 of their papers we have counts for
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cs.LG2022★ 5 cited
Co-Regularized Adversarial Learning for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains. Recent…
cs.LG2021★ 7 cited
Conditional Adversarial Networks for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
In this paper, we propose conditional adversarial networks (CANs), a framework that explores the relationship between the shared features and the label predictions to impose more d…
cs.LG2020★ 14 cited
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Recent advances on unsupervised domain adaptation (UDA) rely on adversarial learning to disentangle the explanatory and transferable features for domain adaptation. However, there…