  Few-shot Learning has been studied to mimic human visual capabilities and
learn effective models without the need of exhaustive human annotation. Even
though the idea of meta-learning for adaptation has dominated the few-shot
learning methods, how to train a feature extractor is still a challenge. In
this paper, we focus on the design of training strategy to obtain an elemental
representation such that the prototype of each novel class can be estimated
from a few labeled samples. We propose a two-stage training scheme,
Partner-Assisted Learning (PAL), which first trains a partner encoder to model
pair-wise similarities and extract features serving as soft-anchors, and then
trains a main encoder by aligning its outputs with soft-anchors while
attempting to maximize classification performance. Two alignment constraints
from logit-level and feature-level are designed individually. For each few-shot
task, we perform prototype classification. Our method consistently outperforms
the state-of-the-art method on four benchmarks. Detailed ablation studies of
PAL are provided to justify the selection of each component involved in
training.
