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Jun, 2024
应对生物数据变异的深度学习模型解释挑战
Challenges in explaining deep learning models for data with biological variation
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Lenka Tětková, Erik Schou Dreier, Robin Malm, Lars Kai Hansen
TL;DR
通过评估基准数据集上的模型并将其应用于真实世界数据,本研究探讨了在生物数据领域应用机器学习模型的挑战以及解释性方法的评估,以检测谷物疾病和损坏。
Abstract
Much
machine learning
research progress is based on developing models and evaluating them on a
benchmark dataset
(e.g., ImageNet for images). However, applying such benchmark-successful methods to real-world data
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