Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning

Anonymous

Mar 06, 2019 ICLR 2019 Workshop RML Blind Submission readers: everyone
  • Keywords: reproducibility, few-shot, machine learning, statistics
  • TL;DR: 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.
  • Abstract: 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.
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