Few-shot learning is a challenging task that aims at training a classifier
for unseen classes with only a few training examples. The main difficulty of
few-shot learning lies in the lack of intra-class diversity within insufficient
training samples. To alleviate this problem, we propose a novel generative
framework, Diversity Transfer Network (DTN), that learns to transfer latent
diversities from known categories and composite them with support features to
generate diverse samples for novel categories in feature space. The learning
problem of the sample generation (i.e., diversity transfer) is solved via
minimizing an effective meta-classification loss in a single-stage network,
instead of the generative loss in previous works.
Besides, an organized auxiliary task co-training over known categories is
proposed to stabilize the meta-training process of DTN. We perform extensive
experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet,
CIFAR100 and CUB. The results show that DTN, with single-stage training and
faster convergence speed, obtains the state-of-the-art results among the
feature generation based few-shot learning methods. Code and supplementary
material are available at: \texttt{this https URL}

提出了一种基于多样性转移网络（DTN）的生成框架，该框架可以将已知类别中的潜在多样性转移并与支持特征组合，以在特征空间中为新类别生成多样化的样本。该框架通过单阶段网络中的有效元分类损失来解决样本生成（即多样性转移）的学习问题，并提出了一个有组织的辅助任务来稳定 DTN 的元训练过程。在三个数据集上的实验和消融研究表明，DTN 可以在单阶段训练和更快的收敛速度下，获得基于特征生成的少样本学习方法的最先进结果。