  Model-Agnostic Meta-Learning (MAML) is a famous few-shot learning method that
has inspired many follow-up efforts, such as ANIL and BOIL. However, as an
inductive method, MAML is unable to fully utilize the information of query set,
limiting its potential of gaining higher generality. To address this issue, we
propose a simple yet effective method that generates psuedo-labels adaptively
and could boost the performance of the MAML family. The proposed methods,
dubbed Generative Pseudo-label based MAML (GP-MAML), GP-ANIL and GP-BOIL,
leverage statistics of the query set to improve the performance on new tasks.
Specifically, we adaptively add pseudo labels and pick samples from the query
set, then re-train the model using the picked query samples together with the
support set. The GP series can also use information from the pseudo query set
to re-train the network during the meta-testing. While some transductive
methods, such as Transductive Propagation Network (TPN), struggle to achieve
this goal.
