An Overview of Deep Learning Architectures in Few-Shot Learning Domain

TL;DR

This paper reviews deep learning architectures tailored for few-shot learning, highlighting recent advances, challenges, and resources to facilitate further research in low-data regimes.

Contribution

It provides a comprehensive overview of deep learning approaches in few-shot learning, including key references, application methods, and open-source resources for new researchers.

Findings

Summarizes recent achievements in few-shot deep learning

Discusses challenges and potential improvements

Provides resources and code for experimentation

Abstract

Since 2012, Deep learning has revolutionized Artificial Intelligence and has achieved state-of-the-art outcomes in different domains, ranging from Image Classification to Speech Generation. Though it has many potentials, our current architectures come with the pre-requisite of large amounts of data. Few-Shot Learning (also known as one-shot learning) is a sub-field of machine learning that aims to create such models that can learn the desired objective with less data, similar to how humans learn. In this paper, we have reviewed some of the well-known deep learning-based approaches towards few-shot learning. We have discussed the recent achievements, challenges, and possibilities of improvement of few-shot learning based deep learning architectures. Our aim for this paper is threefold: (i) Give a brief introduction to deep learning architectures for few-shot learning with pointers to core references. (ii) Indicate how deep learning has been applied to the low-data regime, from data preparation to model training. and, (iii) Provide a starting point for people interested in experimenting and perhaps contributing to the field of few-shot learning by pointing out some useful resources and open-source code. Our code is available at Github: https://github.com/shruti-jadon/Hands-on-One-Shot-Learning.