  In this paper we improve the image embeddings generated in the graph neural
network solution for few shot learning. We propose alternate architectures for
existing networks such as Inception-Net, U-Net, Attention U-Net, and
Squeeze-Net to generate embeddings and increase the accuracy of the models. We
improve the quality of embeddings created at the cost of the time taken to
generate them. The proposed implementations outperform the existing state of
the art methods for 1-shot and 5-shot learning on the Omniglot dataset. The
experiments involved a testing set and training set which had no common classes
between them. The results for 5-way and 10-way/20-way tests have been
tabulated.
