An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
Yaoyao Liu, Bernt Schiele, Qianru Sun
Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. ""Epoch-wise"" means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ""Empirical"" means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent vs. epoch-independent, in the paradigm of meta-learning. We conduct extensive experiments for five-class few-shot learning tasks on three challenging benchmarks: miniImageNet, tieredImageNet, and FC100, and achieve top performance using the epoch-dependent transductive hyperprior learner, which captures the richest information. Our ablation study shows that both ""epoch-wise ensemble"" and ""empirical"" encourage high efficiency and robustness in the model performance."