Building a One-shot Learning Network with PyTorch

Deep learning has been quite popular for image recognition and classification tasks in recent years due to its high performances. However, traditional deep learning approaches usually require a large dataset for the model to be trained on to distinguish very few different classes, which is drastically different from how humans are able to learn from even very few examples. Few-shot or one-shot learning is a categorization problem that aims to classify objects given only a limited amount of samples, with the ultimate goal of creating a more human-like learning algorithm. In this article, we will dive into the deep learning approaches to solving the one-shot learning problem by using a special network structure: Siamese Network. We will build the network using PyTorch and test it on the Omniglot handwritten character dataset and performed several experiments to compare the results of different network structures and hyperparameters, using a one-shot learning evaluation metric.