Description of Invention
In the past decade, deep learning has achieved remarkable performance in image classification. However, deep learning models suffer from generalization and data hungry challenges. These challenges are referred to as the few-shot image classification problem. The main objective of few-shot image classification is to recognize the unseen class images with limited number of labeled samples. In this project, two novel methods have been proposed to address the few-shot image classification task.