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Found 5 papers, 0 papers with code
Learning Target Domain Specific Classifier for Partial Domain Adaptation
Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic.
Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification
KLN can simultaneously learn a more expressive kernel and label prediction distribution, thus, it can be used to improve the classification performance in both supervised and semi-supervised learning scenarios.
Dual Adversarial Auto-Encoders for Clustering
By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.
Domain Adaptation and Image Classification via Deep Conditional Adaptation Network
In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same.
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment
Second, the batch-wise training manner in deep learning limits the description of the global structure.
