An ensemble of epoch-wise empirical Bayes for few-shot learning

• Main Authors: LIU, Yaoyao, SCHIELE, Bernt, SUN, Qianru
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Confidence predictions; Empirical Bayes; Empirical Bayes models; Hyperparameters; Model performance; Predictive models; Robust predictions; Training epochs; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5594; https://ink.library.smu.edu.sg/context/sis_research/article/6597/viewcontent/1904.08479.pdf
• Institution: Singapore Management University

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 \emph{vs.} epoch-independent, in the paradigm of meta-learning. We conduct extensive experiments for five-class few-shot 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