微妙的数据犯罪：天真地训练机器学习算法可能会导致过于乐观的结果
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
摘要：
虽然开放数据库是深度学习（DL）时代的重要资源，但它们有时会被“标签外”使用：为一个任务发布的数据被用于训练另一个任务的算法。这项工作旨在强调，在某些情况下，这种常见的做法可能会导致偏见，过于乐观的结果。我们证明了这种现象的反问题求解器，并显示他们的偏见的性能源于隐藏的数据预处理管道。我们描述了两个典型的开放访问数据库的预处理管道，并研究它们对三个成熟的磁共振成像（MRI）重建算法的影响：压缩感知（CS），字典学习（DictL）和DL。在这项大规模的研究中，我们进行了大量的计算。我们的研究结果表明，CS，DictL和DL算法在对适当的数据进行天真训练时会产生系统性的偏差结果：归一化均方根误差（NRMSE）与预处理程度一致，在某些情况下人为增加了25%-48%。由于这种现象通常是未知的，有偏见的结果有时会作为最先进的技术发表;我们将其称为微妙的数据犯罪。因此，这项工作对大数据的幼稚标签外使用提出了警告，并揭示了现代逆问题求解器对由此产生的偏见的脆弱性。
英文摘要：
While open databases are an important resource in the Deep Learning (DL) era, they are sometimes used "off-label": data published for one task are used for training algorithms for a different one. This work aims to highlight that in some cases, this common practice may lead to biased, overly-optimistic results. We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data preprocessing pipelines. We describe two preprocessing pipelines typical of open-access databases and study their effects on three well-established algorithms developed for Magnetic Resonance Imaging (MRI) reconstruction: Compressed Sensing (CS), Dictionary Learning (DictL), and DL. In this large-scale study we performed extensive computations. Our results demonstrate that the CS, DictL and DL algorithms yield systematically biased results when naively trained on seemingly-appropriate data: the Normalized Root Mean Square Error (NRMSE) improves consistently with the preprocessing extent, showing an artificial increase of 25%-48% in some cases. Since this phenomenon is generally unknown, biased results are sometimes published as state-of-the-art; we refer to that as subtle data crimes. This work hence raises a red flag regarding naive off-label usage of Big Data and reveals the vulnerability of modern inverse problem solvers to the resulting bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
