  Few-shot classification tends to struggle when it needs to adapt to diverse
domains. Due to the non-overlapping label space between domains, the
performance of conventional domain adaptation is limited. Previous work tackles
the problem in a transductive manner, by assuming access to the full set of
test data, which is too restrictive for many real-world applications. In this
paper, we set out to tackle this issue by introducing a inductive framework,
DaFeC, to improve Domain adaptation performance for Few-shot classification via
Clustering. We first build a representation extractor to derive features for
unlabeled data from the target domain (no test data is necessary) and then
group them with a cluster miner. The generated pseudo-labeled data and the
labeled source-domain data are used as supervision to update the parameters of
the few-shot classifier. In order to derive high-quality pseudo labels, we
propose a Clustering Promotion Mechanism, to learn better features for the
target domain via Similarity Entropy Minimization and Adversarial Distribution
Alignment, which are combined with a Cosine Annealing Strategy. Experiments are
performed on the FewRel 2.0 dataset. Our approach outperforms previous work
with absolute gains (in classification accuracy) of 4.95%, 9.55%, 3.99% and
11.62%, respectively, under four few-shot settings.
