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Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance
It is important to have a general method for measuring transferability that can be applied in a variety of situations, such as selecting the best self-supervised pre-trained models that do not have classifiers, and selecting the best transferring layer for a target task.
Fast and Accurate Transferability Measurement for Heterogeneous Multivariate Data
We address the problem of measuring transferability between source and target datasets, where the source and the target have different feature spaces and distributions.
Transfer Alignment Network for Double Blind Unsupervised Domain Adaptation
In this paper, we define the problem of unsupervised domain adaptation under double blind constraint, where either the source or the target domain cannot observe the data in the other domain, but data from both domains are used for training.
