Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed. We propose a methodology for testing for statistical differences in model performances under several replications.To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.We find that the selected implementations exhibit stability across random seed, and repeats.
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.This paper studies reproducibility for few-shot learning.