Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions
This work addresses the challenge of learning from few examples in machine learning, offering an incremental improvement for domain-specific applications like one-shot learning.
The paper tackled the problem of one-shot learning with limited training data by improving Siamese networks using kernel-based activation functions (Kafnets), achieving stronger results than ReLU-based models in embeddings structure, loss convergence, and accuracy.
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.