Prototype Propagation Networks (PPN) for
Weakly-supervised Few-shot Learning on Category Graph
Abstract
A variety of machine learning applications expect
to achieve rapid learning from a limited number of
labeled data. However, the success of most current models is the result of heavy training on big
data. Meta-learning addresses this problem by extracting common knowledge across different tasks
that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised
information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled
data can significantly improve the performance of
meta-learning on few-shot classification. We propose prototype propagation network (PPN) trained
on few-shot tasks together with data annotated by
coarse-label. Given a category graph of the targeted fine-classes and some weakly-labeled coarseclasses, PPN learns an attention mechanism which
propagates the prototype of one class to another on
the graph, so that the K-nearest neighbor (KNN)
classifier defined on the propagated prototypes results in high accuracy across different few-shot
tasks. The training tasks are generated by subgraph
sampling, and the training objective is obtained by
accumulating the level-wise classification loss on
the subgraph. On two benchmarks, PPN signifi-
cantly outperforms most recent few-shot learning
methods in different settings, even when they are
also allowed to train on weakly-labeled data