SMU Research Data Repository (RDR)
Browse
1/1
5 files

Data from: An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning

dataset
posted on 2022-09-28, 02:54 authored by Yaoyao LIU, Bernt SCHIELE, Qianru SUNQianru SUN

PyTorch implementation of "An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning"

This repository contains the PyTorch implementation for the ECCV 2020 Paper "An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning".

 

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 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.

History

Confidential or personally identifiable information

  • I confirm that the uploaded data has no confidential or personally identifiable information.

Usage metrics

    School of Computing and Information Systems

    Categories

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC