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重新评估深度学习 (和元学习) 计算机视觉作为一种有效的方法,以确定骨表面修改中的语音代理

Manuel Domínguez-Rodrigo^1,2,3, Gabriel Cifuentes-Alcobendas^1,2, Marina Vegara-Riquelme^1,2

¹Institute of Evolution in Africa (IDEA), Rice University and Archaeological and Paleontological Museum of the Community of Madrid, 28010, Spain.

Biology methods & protocols

|August 13, 2025

概括

深度学习 (DL) 模型在语音学研究中有效地分析骨表面修饰 (BSM). 本研究通过解决对数据集偏差和模型过拟合的批评来验证DL的有效性,证实了可靠的BSM差异化.

科学领域:

古生物学的古生物学
考古学的考古学
计算机科学计算机科学

背景情况:

陶声学研究重建了古生物学遗迹的保存和修改过程.
最近的批评质疑深度学习 (DL) 对分析骨表面修饰 (BSM) 的可靠性,因为数据集的局限性和潜在的模型偏差.
这些批评可能忽略了小,不平衡的数据集如何导致不适合模型和编码错误可以引入偏差.

研究的目的:

重新评估在语音学研究中DL的效率和分辨率.
为了解决有关DL模型可靠性对BSM分析的批评.
为了调查应用到taphonomic数据集的DL模型中的潜在偏差和过拟合.

主要方法:

复制原始DL模型作为基线模型进行比较.
开发了优化的DL模型,以解决质量差的图像和验证集过拟合的问题.
实施了增强的图像数据增强,k-fold交叉验证和少数镜头学习 (监督和无模型的超级学习) 以进行强大的验证.

主要成果:

复制的DL模型显示了与原始基线模型一致和可比的结果.
经过优化模型,结果几乎不变,即使使用最近生成的BSM图像作为测试集.
这些发现强化了原始DL模型在区分BSM方面的有效性,驳斥了方法过度拟合的说法.

结论:

最初的DL模型在区分BSM方面表现出细微的有效性,尽管数据集的局限性.
关于TAPHONOMY中的DL可靠性的批评可能源于处理小,不平衡的数据集的方法错误.
用更大,更高质量的数据集进行未来的研究可以进一步提高 DL 模型的概括性和可靠性.

关键词:

骨表面的修改骨表面的修改深度学习是一种深度学习.组合学习组合学习公平结局的情况.这就是meta-learning.塔法诺米 (taphonomy) 是一种语法学.

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