  As transfer learning techniques are increasingly used to transfer knowledge
from the source model to the target task, it becomes important to quantify
which source models are suitable for a given target task without performing
computationally expensive fine tuning. In this work, we propose HASTE (HArd
Subset TransfErability), a new strategy to estimate the transferability of a
source model to a particular target task using only a harder subset of target
data. By leveraging the internal and output representations of model, we
introduce two techniques, one class agnostic and another class specific, to
identify harder subsets and show that HASTE can be used with any existing
transferability metric to improve their reliability. We further analyze the
relation between HASTE and the optimal average log likelihood as well as
negative conditional entropy and empirically validate our theoretical bounds.
Our experimental results across multiple source model architectures, target
datasets, and transfer learning tasks show that HASTE modified metrics are
consistently better or on par with the state of the art transferability
metrics.
