6 papers
Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization
Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan
Domain generalization approaches aim to learn a domain invariant prediction model for unknown target domains from multiple training source domains with different distributions. Sig…
Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks
Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan
Unsupervised domain adaptation seeks to mitigate the distribution discrepancy between source and target domains, given labeled samples of the source domain and unlabeled samples of…
Deep Domain Generalization with Feature-norm Network
Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan
In this paper, we tackle the problem of training with multiple source domains with the aim to generalize to new domains at test time without an adaptation step. This is known as do…
Correlation-aware Adversarial Domain Adaptation and Generalization
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh +1
Domain adaptation (DA) and domain generalization (DG) have emerged as a solution to the domain shift problem where the distribution of the source and target data is different. The…
On Minimum Discrepancy Estimation for Deep Domain Adaptation
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh +1
In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification an…
Multi-component Image Translation for Deep Domain Generalization
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh +1
Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only di…