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Found 6 papers, 1 papers with code
Latent Feature Disentanglement For Visual Domain Generalization
We demonstrate the effectiveness of our approach on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in the source and the target domains to better align the source and target distributions.
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
The latter can be achieved by minimizing the maximum discrepancy of predictors (classifiers).
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Synthetic-to-Real Translation
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Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain.
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories
This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems.
Probabilistic Temporal Subspace Clustering
In this paper, we propose a unified non-parametric generative framework for temporal subspace clustering to segment data drawn from a sequentially ordered union of subspaces that deals with the missing features in a principled way.
