  The use of meta-learning and transfer learning in the task of few-shot image
classification is a well researched area with many papers showcasing the
advantages of transfer learning over meta-learning in cases where data is
plentiful and there is no major limitations to computational resources. In this
paper we will showcase our experimental results from testing various
state-of-the-art transfer learning weights and architectures versus similar
state-of-the-art works in the meta-learning field for image classification
utilizing Model-Agnostic Meta Learning (MAML). Our results show that both
practices provide adequate performance when the dataset is sufficiently large,
but that they both also struggle when data sparsity is introduced to maintain
sufficient performance. This problem is moderately reduced with the use of
image augmentation and the fine-tuning of hyperparameters. In this paper we
will discuss: (1) our process of developing a robust multi-class convolutional
neural network (CNN) for the task of few-shot image classification, (2)
demonstrate that transfer learning is the superior method of helping create an
image classification model when the dataset is large and (3) that MAML
outperforms transfer learning in the case where data is very limited. The code
is available here: github.com/JBall1/Few-Shot-Limited-Data
