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Found 4 papers, 0 papers with code
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.
Transfer Learning for Aided Target Recognition: Comparing Deep Learning to other Machine Learning Approaches
Our goal is to address this shortcoming by comparing transfer learning within a DL framework to other ML approaches across transfer tasks and datasets.
Automatic selection of eye tracking variables in visual categorization in adults and infants
With its tight link to visual attention, eye tracking is a promising method for getting access to the mechanisms of category learning.
