meta-learning for medium-shot sparse learning via deep kernels

Few-shot learning assumes that we have a very small dataset for each task and trains a model on the set of tasks. for real-world problems, however, the amount of available data is substantially much more; we call this a medium-shot setting, where the dataset often has several hundreds of data. despite their high accuracy, deep neural networks have a drawback as they are black-box. learning interpretable models has become more important over time. this study aims to obtain sample-based interpretability using the attention mechanism. the main idea is reducing the task training data into a small number of support vectors using sparse kernel methods, and the model then predicts the test data of the task based on these support vectors. we propose a sparse medium-shot learning algorithm based on a metric-based bayesian meta-learning algorithm whose output is probabilistic. sparsity, along with uncertainty, effectively plays a key role in interpreting the model's behavior. in our experiments, we show that the proposed method provides significant interpretability by selecting a small number of support vectors and, at the same time, has a competitive accuracy compared to other less interpretable methods.