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Domain Adaptation by Topology Regularization
While these methods typically require large amounts of labeled training data, domain adaptation (DA) or transfer learning (TL) enables these algorithms to transfer knowledge from a labelled (source) data set to an unlabelled but related (target) data set of interest.
Flexible deep transfer learning by separate feature embeddings and manifold alignment
We also provide practical guidelines for training the network while overcoming vanishing gradients which inhibit learning in some adversarial training settings.
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