  In recent works, utilizing a deep network trained on meta-training set serves
as a strong baseline in few-shot learning. In this paper, we move forward to
refine novel-class features by finetuning a trained deep network. Finetuning is
designed to focus on reducing biases in novel-class feature distributions,
which we define as two aspects: class-agnostic and class-specific biases.
Class-agnostic bias is defined as the distribution shifting introduced by
domain difference, which we propose Distribution Calibration Module(DCM) to
reduce. DCM owes good property of eliminating domain difference and fast
feature adaptation during optimization. Class-specific bias is defined as the
biased estimation using a few samples in novel classes, which we propose
Selected Sampling(SS) to reduce. Without inferring the actual class
distribution, SS is designed by running sampling using proposal distributions
around support-set samples. By powering finetuning with DCM and SS, we achieve
state-of-the-art results on Meta-Dataset with consistent performance boosts
over ten datasets from different domains. We believe our simple yet effective
method demonstrates its possibility to be applied on practical few-shot
applications.
