  Over the past decade, the field of machine learning has experienced
remarkable advancements. While image recognition systems have achieved
impressive levels of accuracy, they continue to rely on extensive training
datasets. Additionally, a significant challenge has emerged in the form of poor
out-of-distribution performance, which necessitates retraining neural networks
when they encounter conditions that deviate from their training data. This
limitation has notably contributed to the slow progress in self-driving car
technology. These pressing issues have sparked considerable interest in methods
that enable neural networks to learn effectively from limited data. This paper
presents the outcomes of an extensive investigation designed to compare two
distinct approaches, transfer learning and meta learning, as potential
solutions to this problem. The overarching objective was to establish a robust
criterion for selecting the most suitable method in diverse machine learning
scenarios. Building upon prior research, I expanded the comparative analysis by
introducing a new meta learning method into the investigation. Subsequently, I
assessed whether the findings remained consistent under varying conditions.
Finally, I delved into the impact of altering the size of the training dataset
on the relative performance of these methods. This comprehensive exploration
has yielded insights into the conditions favoring each approach, thereby
facilitating the development of a criterion for selecting the most appropriate
method in any given situation
