  The lack of a large amount of training data has always been the constraining
factor in solving a lot of problems in machine learning, making One Shot
Learning one of the most intriguing ideas in machine learning. It aims to learn
information about object categories from one, or only a few training examples.
This process of learning in deep learning is usually accomplished by proper
objective function, i.e; loss function and embeddings extraction i.e;
architecture. In this paper, we discussed about metrics based deep learning
architectures for one shot learning such as Siamese neural networks and present
a method to improve on their accuracy using Kafnets (kernel-based
non-parametric activation functions for neural networks) by learning proper
embeddings with relatively less number of epochs. Using kernel activation
functions, we are able to achieve strong results which exceed those of ReLU
based deep learning models in terms of embeddings structure, loss convergence,
and accuracy.
