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Found 6 papers, 4 papers with code
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
Understanding unsupervised domain adaptation has been an important task that has been well explored.
Curriculum based Dropout Discriminator for Domain Adaptation
However, here we suggest that rather than using a point estimate, it would be useful if a distribution based discriminator could be used to bridge this gap.
Ranked #31 on
Domain Adaptation
on Office-31
Attending to Discriminative Certainty for Domain Adaptation
In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain.
Ranked #11 on
Domain Adaptation
on ImageCLEF-DA
Looking back at Labels: A Class based Domain Adaptation Technique
Our observation relies on the analysis that shows that if the discriminator has access to all the information available including the class structure present in the source dataset, then it can guide the transformation of features of the target set of classes to a more structure adapted space.
Ranked #14 on
Domain Adaptation
on ImageCLEF-DA
Multimodal Differential Network for Visual Question Generation
Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations.
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
