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Found 6 papers, 1 papers with code
On Minimum Discrepancy Estimation for Deep Domain Adaptation
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 and recognition tasks.
Multi-component Image Translation for Deep Domain Generalization
If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task.
Ranked #115 on
Domain Generalization
on PACS
Correlation-aware Adversarial Domain Adaptation and Generalization
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.
Ranked #14 on
Domain Adaptation
on ImageCLEF-DA
Deep Domain Generalization with Feature-norm Network
To tackle the aforementioned problem, we introduce an end-to-end feature-norm network (FNN) which is robust to negative transfer as it does not need to match the feature distribution among the source domains.
Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks
This network can achieve source to target domain matching by capturing semantic information at the feature level and producing images for unsupervised domain adaptation from both the source and the target domains.
Generative Adversarial Network
Unsupervised Domain Adaptation
Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization
Domain generalization approaches aim to learn a domain invariant prediction model for unknown target domains from multiple training source domains with different distributions.
