  The successful application of deep learning to many visual recognition tasks
relies heavily on the availability of a large amount of labeled data which is
usually expensive to obtain. The few-shot learning problem has attracted
increasing attention from researchers for building a robust model upon only a
few labeled samples. Most existing works tackle this problem under the
meta-learning framework by mimicking the few-shot learning task with an
episodic training strategy. In this paper, we propose a new transfer-learning
framework for semi-supervised few-shot learning to fully utilize the auxiliary
information from labeled base-class data and unlabeled novel-class data. The
framework consists of three components: 1) pre-training a feature extractor on
base-class data; 2) using the feature extractor to initialize the classifier
weights for the novel classes; and 3) further updating the model with a
semi-supervised learning method. Under the proposed framework, we develop a
novel method for semi-supervised few-shot learning called TransMatch by
instantiating the three components with Imprinting and MixMatch. Extensive
experiments on two popular benchmark datasets for few-shot learning,
CUB-200-2011 and miniImageNet, demonstrate that our proposed method can
effectively utilize the auxiliary information from labeled base-class data and
unlabeled novel-class data to significantly improve the accuracy of few-shot
learning task.
