Low-shot Image Classification Using Mixture of Experts

Abstract

We propose a low-shot image classification method called Limo, which can train an accurate image classification model under conditions of acute data scarcity. Limo uniquely assembles existing knowledge from a set of diverse models and builds a novel mixture of experts architecture for low-shot image classification. Limo’s architecture introduces minimal number of new model parameters, such that the added parameters can be tuned even with little training data. This characteristic is especially important because, by definition, there is insufficient data in the low-shot setting to learn new model parameters. We demonstrate with extensive experimental evaluation that Limo achieves remarkable accuracy compared to state-of-the-art zero-shot and high-shot image classification approaches.