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Co-Regularized Adversarial Learning for Multi-Domain Text Classification
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.
Maximum Batch Frobenius Norm for Multi-Domain Text Classification
Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning.
Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation
Adversarial domain adaptation has made impressive advances in transferring knowledge from the source domain to the target domain by aligning feature distributions of both domains.
Conditional Adversarial Networks for Multi-Domain Text Classification
We provide theoretical analysis for the CAN framework, showing that CAN's objective is equivalent to minimizing the total divergence among multiple joint distributions of shared features and label predictions.
Mixup Regularized Adversarial Networks for Multi-Domain Text Classification
Using the shared-private paradigm and adversarial training has significantly improved the performances of multi-domain text classification (MDTC) models.
Dual Adversarial Training for Unsupervised Domain Adaptation
Domain adaptation sets out to address this problem, aiming to leverage labeled data in the source domain to learn a good predictive model for the target domain whose labels are scarce or unavailable.
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
Second, samples from the source and target domains alone are not sufficient for domain-invariant feature extracting in the latent space.
