Although large-scale datasets have driven progress of deep learning, there are also many domains like medical imagery where such large-scale datasets do not exist. These domains motivated research on meta-learning, where few-shot learners are trained on a different domain. However, complex meta-learning algorithms can fail to outperform the naive transfer learning method when the difference between tasks and domains are extreme.