DPGN: Distribution Propagation Graph Network for Few-shot Learning

Ling Yang, LiangLiang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou

Abstract:

Most graph-network-based meta-learning approaches
model instance-level relation of examples. We extend this
idea further to explicitly model the distribution-level relation of one example to all other examples in a 1-vs-N manner. We propose a novel approach named distribution propagation graph network (DPGN) for few-shot learning. It
conveys both the distribution-level relations and instancelevel relations in each few-shot learning task. To combine
the distribution-level relations and instance-level relations
for all examples, we construct a dual complete graph network which consists of a point graph and a distribution
graph with each node standing for an example. Equipped
with dual graph architecture, DPGN propagates label information from labeled examples to unlabeled examples within
several update generations. In extensive experiments on
few-shot learning benchmarks, DPGN outperforms stateof-the-art results by a large margin in 5% ∼ 12% under
supervised settings and 7% ∼ 13% under semi-supervised
settings. Code is available at https://github.com/megviiresearch/DPGN