Deep learning still struggles with certain kinds of scientific data. Notably, pretraining data may not provide coverage of relevant distribution shifts (e.g., shifts induced via the use of different measurement instruments). We consider deep learning models trained to classify the synthesis conditions of uranium ore concentrates (UOCs) and show that model editing is particularly effective for improving generalization to distribution shifts common in this domain. In particular, model editing outperforms finetuning on two curated datasets comprising of micrographs taken of U$_{3}$O$_{8}$ aged in humidity chambers and micrographs acquired with different scanning electron microscopes, respectively.

翻译：深度学习在某些类型的科学数据上仍面临挑战。值得注意的是，预训练数据可能无法覆盖相关的分布偏移（例如，由使用不同测量仪器引起的偏移）。本文研究了用于分类铀矿石浓缩物合成条件的深度学习模型，并证明模型编辑对于提升该领域常见分布偏移的泛化能力尤为有效。具体而言，在两组精选数据集上，模型编辑的表现均优于微调方法：第一组数据集包含在湿度箱中老化的U$_{3}$O$_{8}$样品的显微图像，第二组数据集则包含使用不同扫描电子显微镜获取的显微图像。