Learning to Learn: Model Regression Networks for Easy Small Sample Learning

Abstract

We develop a conceptually simple but powerful approach that can learn novel categories from few annotated examples. In this approach, the experience with already learned categories is used to facilitate the learning of novel classes. Our insight is two-fold: (1) there exists a generic, category agnostic transformation from models learned from few samples to models learned from large enough sample sets, and (2) such a transformation could be effectively learned by high-capacity regressors. In particular, we automatically learn the transformation with a deep model regression network on a large collection of model pairs. Experiments demonstrate that encoding this transformation as prior knowledge greatly facilitates the recognition in the small sample size regime on a broad range of tasks, including domain adaptation, fine-grained recognition, action recognition, and scene classification.