  In this paper, we look at the problem of cross-domain few-shot classification
that aims to learn a classifier from previously unseen classes and domains with
few labeled samples. Recent approaches broadly solve this problem by
parameterizing their few-shot classifiers with task-agnostic and task-specific
weights where the former is typically learned on a large training set and the
latter is dynamically predicted through an auxiliary network conditioned on a
small support set. In this work, we focus on the estimation of the latter, and
propose to learn task-specific weights from scratch directly on a small support
set, in contrast to dynamically estimating them. In particular, through
systematic analysis, we show that task-specific weights through parametric
adapters in matrix form with residual connections to multiple intermediate
layers of a backbone network significantly improves the performance of the
state-of-the-art models in the Meta-Dataset benchmark with minor additional
cost.
