  Meta-learning has been widely used for implementing few-shot learning and
fast model adaptation. One kind of meta-learning methods attempt to learn how
to control the gradient descent process in order to make the gradient-based
learning have high speed and generalization. This work proposes a method that
controls the gradient descent process of the model parameters of a neural
network by limiting the model parameters in a low-dimensional latent space. The
main challenge of this idea is that a decoder with too many parameters is
required. This work designs a decoder with typical structure and shares a part
of weights in the decoder to reduce the number of the required parameters.
Besides, this work has introduced ensemble learning to work with the proposed
approach for improving performance. The results show that the proposed approach
is witnessed by the superior performance over the Omniglot classification and
the miniImageNet classification tasks.
