  Few-shot learning aims to train a classifier that can generalize well when
just a small number of labeled examples per class are given. We introduce a
transductive maximum margin classifier for few-shot learning (FS-TMMC). The
basic idea of the classical maximum margin classifier is to solve an optimal
prediction function so that the training data can be correctly classified by
the resulting classifer with the largest geometric margin. In few-shot
learning, it is challenging to find such classifiers with good generalization
ability due to the insufficiency of training data in the support set. FS-TMMC
leverages the unlabeled query examples to adjust the separating hyperplane of
the maximum margin classifier such that the prediction function is optimal on
both the support and query sets. Furthermore, we use an efficient and effective
quasi-Newton algorithm, the L-BFGS method for optimization. Experimental
results on three standard few-shot learning benchmarks including miniImagenet,
tieredImagenet and CUB show that our method achieves state-of-the-art
performance.
