  Learning to generate a task-aware base learner proves a promising direction
to deal with few-shot learning (FSL) problem. Existing methods mainly focus on
generating an embedding model utilized with a fixed metric (eg, cosine
distance) for nearest neighbour classification or directly generating a linear
classier. However, due to the limited discriminative capacity of such a simple
metric or classifier, these methods fail to generalize to challenging cases
appropriately. To mitigate this problem, we present a novel deep metric
meta-generation method that turns to an orthogonal direction, ie, learning to
adaptively generate a specific metric for a new FSL task based on the task
description (eg, a few labelled samples). In this study, we structure the
metric using a three-layer deep attentive network that is flexible enough to
produce a discriminative metric for each task. Moreover, different from
existing methods that utilize an uni-modal weight distribution conditioned on
labelled samples for network generation, the proposed meta-learner establishes
a multi-modal weight distribution conditioned on cross-class sample pairs using
a tailored variational autoencoder, which can separately capture the specific
inter-class discrepancy statistics for each class and jointly embed the
statistics for all classes into metric generation. By doing this, the generated
metric can be appropriately adapted to a new FSL task with pleasing
generalization performance. To demonstrate this, we test the proposed method on
four benchmark FSL datasets and gain surprisingly obvious performance
improvement over state-of-the-art competitors, especially in the challenging
cases, eg, improve the accuracy from 26.14% to 46.69% in the 20-way 1-shot task
on miniImageNet, while improve the accuracy from 45.2% to 68.72% in the 5-way
1-shot task on FC100. Code is available: https://github.com/NWPUZhoufei/DAM.
