  Given a set of heterogeneous source datasets with their classifiers, how can
we quickly find the most useful source dataset for a specific target task? We
address the problem of measuring transferability between source and target
datasets, where the source and the target have different feature spaces and
distributions. We propose Transmeter, a fast and accurate method to estimate
the transferability of two heterogeneous multivariate datasets. We address
three challenges in measuring transferability between two heterogeneous
multivariate datasets: reducing time, minimizing domain gap, and extracting
meaningful homogeneous representations. To overcome the above issues, we
utilize a pre-trained source model, an adversarial network, and an
encoder-decoder architecture. Extensive experiments on heterogeneous
multivariate datasets show that Transmeter gives the most accurate
transferability measurement with up to 10.3 times faster performance than its
competitor. We also show that selecting the best source data with Transmeter
followed by a full transfer leads to the best transfer accuracy and the fastest
running time.
