A Pseudo-code of OOD-MAML Algorithm 1 OOD-MAML with K-shot training samples

Omniglot (Lake et al., 2015) is a dataset of handwritten characters and contains 20 examples of 1623 characters. Omniglot is the most commonly used dataset in few-shot learning, and its images are resized to 28 28 (Finn et al., 2017; Santoro et al., 2016; Snell et al., 2017; Sung et al., 2018; Koch et al., 2015). As in other studies, we randomly select 1200 characters for meta-training and use the remaining for meta-testing. It contains a total of 60K images of 100 different classes, each of which comprises 600 RGB images. Ravi and Larochelle (2016) presented the protocol for mini ImageNet as per which all the images are downsampled to 84 84 and are divided into 64 classes for meta-training, 16 classes for meta-validation, and 20 for meta-testing. We followed this protocol but did not use the meta-validation set.