TorchMetrics - Measuring Reproducibility in PyTorch

A main problem with reproducing machine learning publications is the variance of metric implementations across papers. A lack of standardization leads to different behavior in mechanisms such as checkpointing, learning rate schedulers or early stopping, that will influence the reported results. For example, a complex metric such as Fréchet inception distance (FID) for synthetic image quality evaluation (Heusel et al., 2017) will differ based on the specific interpolation method used.

• Publication year

  2022

• Venue

  Journal of Open Source Software

• Publication date

  2022-02-11

• Fields of study

  Computer Science

• Identifiers
  DOI 10.21105/joss.04101
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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