Ensemble-Based Deep Metric Learning for Few-Shot Learning

Abstract

Overfitting is an inherent problem in few-shot learning. Ensemble learning integrates multiple machine learning models to improve the overall prediction ability on limited data and hence alleviates the problem of overfitting effectively. Therefore, we apply the idea of ensemble learning to few-shot learning to improve the accuracy of few-shot classification. Metric learning is an important means to solve the problem of few-shot classification. In this paper, we propose ensemble-based deep metric learning (EBDM) for few-shot learning, which is trained end-to-end from scratch. We split the feature extraction network into two parts: the shared part and exclusive part. The shared part is the lower layers of the feature extraction network and is shared across ensemble members to reduce the number of parameters. The exclusive part is the higher layers of the feature extraction network and is exclusive to each individual learner. The coupling of the two parts naturally forces any diversity between the ensemble members to be concentrated on the deeper, unshared layers. We can obtain different features from the exclusive parts and then use these different features to compute diverse metrics. Combining these multiple metrics together will generate a more accurate ensemble metric. This ensemble metric can be used to assign labels to images of new classes with a higher accuracy. Our work leads to a simple, effective, and efficient framework for few-shot classification. The experimental results show that our approach attains superior performance, with the largest improvement of in classification accuracy over related competitive baselines.