  Given an initial recognition model already trained on a set of base classes,
the goal of this work is to develop a meta-model for few-shot learning. The
meta-model, given as input some novel classes with few training examples per
class, must properly adapt the existing recognition model into a new model that
can correctly classify in a unified way both the novel and the base classes. To
accomplish this goal it must learn to output the appropriate classification
weight vectors for those two types of classes. To build our meta-model we make
use of two main innovations: we propose the use of a Denoising Autoencoder
network (DAE) that (during training) takes as input a set of classification
weights corrupted with Gaussian noise and learns to reconstruct the
target-discriminative classification weights. In this case, the injected noise
on the classification weights serves the role of regularizing the weight
generating meta-model. Furthermore, in order to capture the co-dependencies
between different classes in a given task instance of our meta-model, we
propose to implement the DAE model as a Graph Neural Network (GNN). In order to
verify the efficacy of our approach, we extensively evaluate it on ImageNet
based few-shot benchmarks and we report strong results that surpass prior
approaches. The code and models of our paper will be published on:
https://github.com/gidariss/wDAE_GNN_FewShot
